Apparatus for controlling vehicle driving power

An engine controller includes a accelerator stroke detector, vehicle speed and vehicle acceleration sensors, first and second model learning units and a drive control unit. The first model learning unit constructs a vehicle acceleration model that exhibits and updates vehicle acceleration characteristics as required by a driver. The first unit includes a first learning program for updating acceleration model, and generates a first output (Gx) in response to the detected accelerator stroke and vehicle speed according to the first program. The first unit calculates a deviation (.DELTA.G) between the detected vehicle acceleration (G) and the first output (Gx), and modifies the first output (Gx) to decrease the deviation (.DELTA.G) to provide an updated acceleration model as required by the driver. The second model learning unit is for constructing a sensitivity model used to regulate the opening angle of an engine throttle valve. The second unit includes a second learning program for updating the sensitivity model and generating a second output (Thx) in response to the detected accelerator stroke and vehicle speed. As a consequence, the output (Thx) is modified to decrease the deviation (.DELTA.G) which provides for an update to the sensitivity model. The drive control unit controls the throttle angle referring to a target value determined on the basis of the second output (Thx).

BACKGROUND OF THE INVENTION 
1. Field of the Invention 
The present invention relates generally to a vehicle driving power 
controller which controls power output of a vehicle so that acceleration 
of the vehicle approaches the acceleration requested by a driver. 
2. Description of the Related Art 
Generally, a vehicle equipped with an engine and wheels is required to 
operate under a variety of running conditions (e.g., various road surface 
and weather conditions) including those imposed on the vehicle by the 
driver. The quickness and smoothness of the vehicle's response to changing 
control input is highly desired. Conventional technology controls 
vehicular driving power based on the magnitude or stroke of urged 
accelerator pedal manipulated in part by the driver. 
Japanese Unexamined Patent Publication No. 1-294925 discloses a driving 
power controller which estimates a target acceleration based upon the 
magnitude of the urged accelerator pedal (i.e., accelerator opening angle) 
manipulated by the driver. The controller computes a difference or 
deviation between the estimated target acceleration and actual 
acceleration. The controller also adjusts an opening degree (i.e., degree 
of angle) of an engine throttle valve by reference to a data map 
correlating the computed deviation and estimated target acceleration. 
Therefore, the actual vehicle acceleration is controlled to approach the 
target acceleration. 
However, any uniform estimate for a target acceleration is set in the data 
map using only the correlation between the accelerator angle and vehicle 
speed. Thus, any particular preset target acceleration referenced in the 
data map may not always match the acceleration or driving power desired by 
an individual driver according to his or her own driving habit, be it 
slow, fast or any combination of the two. 
Japanese Unexamined Patent Publication No. 4-314940 discloses a technology 
which solves the above-described drawbacks, improves efficiency of memory 
usage in the controller and speeds up the computation of target 
acceleration. According to this technology, an opening angle of a linkless 
type throttle valve is controlled according to the magnitude of the urged 
accelerator pedal (i.e., accelerator stroke) manipulated by the driver. A 
backup RAM in the controller stores data in two dimensional map style for 
determining the target acceleration in response to the accelerator stroke. 
Accordingly, the controller controls the opening angle of the throttle 
valve so that the actual vehicular acceleration approaches the target 
acceleration determined by reference to the data map, thereby controlling 
the vehicle driving power. 
With conventional technology, the change in the accelerator stroke relative 
to the actual vehicular acceleration is presumed by the controller to be 
requested by the driver from sensed changes in the degree of acceleration. 
The controller corrects data to be stored in the data map so as to 
minimize the deviation between the requested acceleration by the driver 
and the target acceleration determined through the data map. The 
controller re-stores the corrected data in a backup RAM. Data in the data 
map is revised by correcting the target acceleration relative to the 
above-described deviation. In other words, the controller receives target 
acceleration data in response to changes in the accelerator stroke. Since 
target acceleration data is automatically updated to account for the 
changes in the magnitude of acceleration required by the driver, the 
controller calculates a target acceleration corresponding to a particular 
driver's needs or characteristics. 
However, even in this conventional technology, target acceleration data is 
only corrected or modified and old map data is simply replaced with 
corrected data to update the target acceleration data. That is, when 
target acceleration data is to be updated in the data map, it is done so 
only within the confines of a certain narrow accelerator stroke range for 
a particular time. For example, when the vehicle is cruising at a constant 
speed, the controller updates only the target acceleration data 
corresponding to the accelerator stroke which currently fits the constant 
cruising speed. Likewise, when the vehicle undergoes sudden acceleration, 
the target acceleration data is updated by the controller only according 
to the specific region of the accelerator's stroke range. 
According to the above-described manner by which data is updated in the 
data map, only that portion of the target data corresponding to the 
limited accelerator stroke region is correctly updated. This results in 
only a partially updated data map with part of the map containing updated 
data and part containing old data. Put differently, even if the certain 
regions of the data map contain updated target acceleration data, because 
those regions are updated only with respect to a specific accelerator 
stroke range, other regions of the data map contain target data that 
remains outdated or uncorrected. Consequently, discontinuities in target 
acceleration data may form in the data map with respect to a particular 
range of the accelerator's stroke. Unfortunately, when vehicular drive 
power is controlled by means of a data map having such discontinuous data, 
the vehicle is often unresponsive, or at least, insufficiently responsive 
to the changing acceleration needs of the driver and specifically to the 
changes in the stroke of the accelerator. 
SUMMARY OF THE INVENTION 
Accordingly, it is a primary objective of the present invention to provide 
an improved vehicle driving power control apparatus responsive to changes 
in the stroke of the vehicle's accelerator irrespective of an individual's 
driving habits or changes in road conditions. The improved control 
apparatus enables the data used for the feedback control of vehicle 
driving power to be continuous throughout the entire region of the 
acceleration stroke. 
To achieve the foregoing and other objects and in accordance with the 
purpose of the present invention, an improved apparatus is provided for 
controlling the driving power of a vehicle. 
The vehicle includes an engine or motor mounted thereon, a power adjuster 
such as a throttle valve for gasoline engine system, and a power 
manipulator such as an accelerator with a pedal. The power adjuster is for 
regulating power output from the engine given any particular level of 
engine power, depending on a control parameter set for the power adjuster. 
The power manipulator is in turn manipulated by the vehicle's driver to 
regulate the engine's power output. 
The improved control apparatus comprises a manipulation detector, speed 
detector, acceleration detector, first and second model learning units and 
drive control unit. 
The manipulation detector detects the amount of driver manipulated power 
and outputs a signal indicative of the detected manipulation amount. The 
speed detector is mounted on the vehicle and detects the speed of the 
vehicle to output a signal indicative of the detected vehicle speed. The 
acceleration detector is mounted on the vehicle and detects the 
acceleration of the vehicle to output a signal indicative of the detected 
vehicle acceleration. 
The first model learning unit constructs a vehicle acceleration model by 
processing vehicle acceleration data in response to the driver's changing 
acceleration needs. The first unit determines the relationship among three 
factors: the manipulated power amount determined by the power manipulator, 
the vehicle's speed and the vehicle's acceleration. The first unit 
includes a first program that updates the unit's generated acceleration 
model. The first unit then generates a first output (Gx) in response to 
the detected power manipulation value and the detected speed of the 
vehicle. The first unit calculates a deviation (.DELTA.G) from among the 
detected vehicle acceleration (G) transmitted from the acceleration 
detector, the acceleration (G) being selected as reference data, and the 
first output (Gx). The first unit further normalizes the first output (Gx) 
to decreaae the deviation (.DELTA.G), and thus provides an update to the 
acceleration model. 
The second model learning unit constructs a sensitivity model used to 
regulate the power adjuster's control parameter. The second model 
determines the relation among the manipulation amount of the power 
manipulator, the vehicle speed and the control parameter. A second 
learning program is provided in the second unit for updating the 
sensitivity model and generating a second output (Thx) in response to the 
detected manipulation amount and detected vehicle speed. The second unit 
then normalizes the second output (Thx) to decrease the deviation 
(.DELTA.G) calculated by the first model learning unit, and thereby 
updates the sensitivity model. 
The drive control unit controls the power adjuster which in turn controls 
the vehicle's driving power. The drive control unit determines and adjusts 
the control parameter by reference to a target control parameter 
determined from the second output (Thx) transmitted from the second model 
learning unit. 
It is preferred according to the present invention that each of the first 
and second learning programs incorporate neural network technology. Each 
program in the first and second model learning units utilizes at least one 
weighting coefficient as variable data for use in neural net so as to 
decrease the calculated deviation (.DELTA.G).

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
The first to thirteenth embodiments of the present invention will be 
described below referring to the accompanying drawings. 
First Embodiment 
A first embodiment of this invention will now be described with reference 
to FIGS. 1 through 11. 
As shown in FIG. 1, a gasoline engine 2 is mounted at the front portion of 
a vehicle 1. The engine 2 has a plurality of in-line cylinders. A fuel 
mixture consisting of air and fuel is fed via an air intake passage 3 in a 
combustion chamber to each cylinder in the engine 2, and then is ignited 
and burnt by an ignition plug to move the piston, crankshaft, etc. This 
action provides the output of the engine 2. The gas generated by the 
combustion moves out of the cylinder through an exhaust passage 4. 
The crankshaft of the engine 2 is drivably coupled to a pair of rear wheels 
5 through a transmission, a propeller shaft, a differential gear, a drive 
shaft, etc. A pair of front wheels 6 are interlocked with the manipulation 
of a steering wheel provided at the driver's seat. 
A throttle valve 7 is supported pivotable midway along the air intake 
passage 3 by a pivot 7a. This pivot 7a is coupled to a DC motor 8, 
provided in the vicinity of the throttle valve 7. As the DC motor 8 is 
driven, the throttle valve 7 rotates integrally with the pivot 7a. Through 
this rotation, the angle of the throttle valve 7 (throttle angle Th) is 
adjusted. This angle adjustment regulates the amount of air received by 
each combustion chamber of the engine from the air intake passage 3, and 
also controls the output of the engine 2. 
A throttle sensor is positioned in the proximity of the throttle valve 7 to 
detect the throttle angle Th. An accelerator pedal 10 is provided at the 
driver's seat in the vehicle 1. This accelerator pedal 10 is manipulated 
by a driver DR to control the output of the engine 2 as needed. Provided 
in the vicinity of the accelerator pedal 10 is an accelerator pedal sensor 
11 for detecting the acceleration stroke S or the amount of the 
manipulation. 
An acceleration sensor 12 is included near the center of the vehicle to 
detect the forward or backward acceleration G of the vehicle 1. The front 
wheel 6 is provided with a vehicle speed sensor 13 for detecting the 
vehicle speed v of the vehicle 1 in accordance with the number of 
rotations of the front wheels 6. 
A throttle computer 21 and a neuro computer 22 are used to properly control 
the opening/closing of the throttle valve 7 in response to the request 
made by the driver DR. The computer 21 is connected to the DC motor 8 and 
the throttle sensor 9. The design of computer 22 is structured using the 
neural network technology. Connected to the computer 22 are the 
accelerator pedal sensor 11, the acceleration sensor 12 and the vehicle 
speed sensor 13. Both computers 21 and 22 are mutually connected. 
As shown in FIG. 2, the computer 22 includes a central processing unit 
(CPU) 23, a read only memory (ROM) 24, a random access memory (RAM) 25, a 
backup RAM 26 and an input/output (I/O) interface circuit 27. Those units 
are mutually connected by a bus 28. The ROM 24 holds in advance a learning 
control program based on the neural network technology and initial data. 
The CPU 23 executes various kinds of operations in accordance with the 
learning control program and initial data. The CPU 23 also has a counter 
function. The RAM 25 temporarily stores the results of the operations 
executed by the CPU 23. The backup RAM 26 is backed up by a battery to 
hold various types of data in the RAM 25 even after power supply to the 
computer 22 is stopped. 
The accelerator pedal sensor 11 and the vehicle speed sensor 13 are 
connected to the I/O interface circuit 27. The acceleration sensor 12 is 
also connected to the I/O interface via a low-pass filter 29. The filter 
29 freely passes that component of the detection signal provided by the 
acceleration sensor 12, which has a lower frequency than a predetermined 
cutoff reference frequency, and greatly attenuates a high frequency. The 
computer 21 is connected to the I/O interface circuit 27. 
The CPU 23 receives, as input values, various signals from the individual 
sensors 11 to 13 via the I/O interface circuit 27. Based on the input 
values, the CPU 23 executes learning control of an "acceleration model" in 
response to changes in vehicle acceleration occasioned by the driver DR, 
in accordance with the aforementioned learning control program. The CPU 23 
also executes learning control of a "throttle sensitivity model" according 
to this "acceleration model." The CPU 23 sends out the learning results to 
the computer 21 via the I/O interface circuit 27. 
The configuration of the computer 21 is basically the same as that of the 
computer 22. The computer 21 comprises a CPU 30, a ROM 31, a RAM 32, a 
backup RAM 33, an I/O interface circuit 34 and a bus 35. The DC motor 8, 
the throttle sensor 9 and the computer 22 are connected to the I/O 
interface circuit 34. A throttle angle control program is stored in 
advance in the ROM 31. This program controls the opening/closing of the 
throttle valve 7 based on the learning result of the computer 22 or values 
that are separately set therein. 
The CPU 30 receives, as input values, data of the learning results coming 
from the computer 22 via the I/O interface circuit 34. The CPU 30 
receives, as an input value, a signal sent from the throttle sensor 9 via 
the I/O interface circuit 34. Based on those input values, the CPU 30 
properly controls the DC motor 8 in accordance with the aforementioned 
throttle angle control program. 
The conceptual structure of the neural network technology adapted to the 
computer 22 will be discussed below. 
The network technology in the first embodiment includes two multi-layer 
neural networks as shown in FIGS. 3 and 4. The two networks have 
substantially the same structure; each network includes an "input layer" 
having two neurons n1, an "intermediate layer" having two to ten neurons 
n2, and an "output layer" having a single neuron n3. The individual 
neurons n1, n2 and n3 in the individual layers coupled together by 
synapses sp. In each network, signals flow in one direction from the 
"input layer" to the "intermediate layer", and from the "intermediate 
layer" to the "output layer". At each of the neurons n1, n2 and n3 of the 
individual layers, the neuron state is determined on the basis of the 
signal received from the preceding layer, and the signal is sent to the 
next layer. The output result of each network is the state value of the 
neuron n3 of the "output layer". 
The network shown in FIG. 3 is for learning the acceleration model. In this 
network, the acceleration stroke S detected by the accelerator pedal 
sensor 11 is input to one of the neurons n1 of the "input layer". The 
vehicle speed V, detected by the vehicle speed sensor 13, is input to the 
other neuron n1. The output result of the network, or the state value of 
the neuron n3 of the "output layer", is an acceleration model output Gx. 
In this network, the "weighting coefficients" of the synapses sp are 
corrected based on the acceleration model output Gx. In making the 
correction, the acceleration G of the vehicle detected by the acceleration 
sensor 12 is used as "teaching data" and the acceleration model output Gx 
is compared with the "teaching data". In other words, the vehicle's 
particular acceleration occasioned by the driver DR is represented by the 
actual acceleration G of the vehicle 1, and this acceleration G is used as 
"teaching data" that is to be compared with the acceleration model output 
Gx. An acceleration deviation .DELTA.G (=G-Gx), obtained by the comparison 
of the acceleration model output Gx with the acceleration G, is treated as 
an "error signal". The "weighting coefficients" of all the synapses sp are 
corrected to make the error portion of this "error signal" smaller. 
Neuro computer 22 learns the relationship among the acceleration stroke S, 
the vehicle speed V and the acceleration G as the "acceleration model" 
when driver DR occasions changes to the vehicle's acceleration and when 
deviation in the "teaching data" become smaller. That is, the 
"acceleration model" is learned as illustrated in FIG. 5 when the 
acceleration model output Gx approaches the acceleration G. 
The network shown in FIG. 4 is for learning the throttle sensitivity model. 
In this network, the acceleration stroke S is input to one of the neurons 
n1 of the "input layer" and the vehicle speed V is input to the other 
neuron n1. The output result of the network, or the state value of the 
neuron n3 of the "output layer", is a throttle sensitivity model output 
Thx. 
In this network, the deviation (acceleration deviation .DELTA.G) between 
the acceleration G and the acceleration model output Gx is an "error 
signal". The "weighting coefficients" of all the synapses sp are corrected 
so as to make the error portion of this "error signal" smaller. In other 
words, the relationship among the acceleration stroke S, the vehicle speed 
V and the throttle angle Th is learned as the "throttle sensitivity model" 
according to throttle changes occasioned by the driver DR. This 
sensitivity model is designed to make the error portion of the "error 
signal" smaller. That is, the "throttle sensitivity model" is learned as 
the characteristic shown in FIG. 6. 
The above-described conceptual structure of the neural network is merely 
given for the sake of convenience. The core of the network lies in the 
learning control program, which is stored in advance in the ROM 24 of the 
computer 22. The network is realized by mathematical operations in the 
learning control program. A typical "error feedback learning algorithm" is 
applied to the learning control program. In the first embodiment, the 
learning control program is prepared to finally obtain the relationship 
between the acceleration stroke S and the throttle sensitivity Thg as 
shown in FIG. 7. 
A description will now be given of operations for learning the 
"acceleration model", the "throttle sensitivity model" and other models 
that are executed by the computer 22. FIG. 8 shows a flowchart 
illustrating the "learning control routine" in the learning control 
program, which is run by the computer 22. This routine is executed 
repeatedly at a given period, for example, "0.1 sec", once the routine 
starts. 
When "0.1 sec" passes from the initialization of the previous "learning 
control routine" cycle, the computer 22 reads the acceleration stroke S, 
acceleration G and vehicle speed V based on various signals from the 
accelerator pedal sensor 11, acceleration sensor 12 and vehicle speed 
sensor 13 in step 101. 
In the next step 102, the computer 22 executes the learning of the 
"throttle sensitivity model" to obtain the throttle sensitivity model 
output Thx and determines the throttle sensitivity Thg based on that value 
Thx. More specifically, the computer 22 acquires the throttle sensitivity 
model output Thx from the characteristic (see FIG. 6) of the already 
learned "throttle sensitivity model" based on the acceleration stroke S 
and vehicle speed V as input values, and determines the throttle 
sensitivity Thg by the following equation (1). 
EQU Thg=.varies.1+Thx*K (1) 
where .varies.1 is a reference value that is set to "1.0" in the first 
embodiment and K is a positive constant. 
The computer 22 sends the throttle sensitivity Thg and acceleration stroke 
S to the computer 21 in step 103. Alternatively, the computer 22 
multiplies the throttle sensitivity Thg by the acceleration stroke S to 
obtain a target throttle angle Thg.S and sends the value to the computer 
21. 
Next, the computer 22 learns the "acceleration model" from changes in the 
vehicle's acceleration occasioned by the driver DR in step 104. More 
specifically, the computer 22 sets the acceleration G of the vehicle 1, 
detected by the acceleration sensor 12, as "teaching data". In addition, 
the computer 22 learns the relationship among the acceleration stroke S, 
the vehicle speed V and the acceleration G as the "acceleration model", 
occasioned by the driver's DR continued alteration of the vehicle's 
acceleration and compares it against the previously learned "teaching 
data". Computer 22 can thereby learn by reducing the number of inherent 
deviations among the data comprising the "teaching data". 
Suppose that the curve indicated by the solid line in FIG. 5 is the 
characteristic of the current "acceleration model". Also suppose that the 
driver DR pushes the accelerator pedal 10 down to run the vehicle 1 faster 
and the acceleration G of the vehicle 1 becomes greater than the current 
acceleration model output Gx. The acceleration G at this time is the newly 
occasioned acceleration, and the current characteristic indicated by the 
solid line in FIG. 5 is altered to a new characteristic indicated by the 
broken line. That is, the whole relationship among the acceleration stroke 
S, the vehicle speed V and the acceleration model output Gx is learned as 
a continuous model. This characteristic will not be partially 
discontinuous. 
When the vehicle speed V is "0", as shown in FIG. 5, the relationship among 
the entire range of the acceleration stroke S, the entire range of the 
vehicle speed V and the acceleration G of the vehicle 1 is learned from 
the "acceleration model". 
The computer 22 then obtains the deviation (acceleration deviation 
.DELTA.G) between the acceleration G and the acceleration model output Gx, 
and learns the "throttle sensitivity model" using that value as an "error 
signal" in step 105. In other words, the computer 22 learns the 
relationship among the acceleration stroke S, the vehicle speed V and the 
throttle angle Th as the "throttle sensitivity model" in such a way as to 
reduce the error portion of the error signal". 
Suppose that the straight line indicated by the solid line in FIG. 6 is the 
initial value of the "throttle sensitivity model" When the driver DR 
pushes the accelerator pedal 10 down to run the vehicle 1 faster, the 
acceleration G of the vehicle 1 increases, generating a deviation between 
this acceleration G and the acceleration model output Gx. This deviation 
(acceleration deviation .DELTA.G) is used as the "error signal" and the 
throttle sensitivity model is so learned as to cause a reduction in the 
error portion of signal .DELTA.G. As a result of this learning process by 
computer 21, the "throttle sensitivity model" is altered to the curve 
indicated by the broken line from the initial value indicated by the solid 
line in FIG. 6. That is, the whole relationship among the acceleration 
stroke S, the vehicle speed V and the throttle sensitivity model output 
Thx is learned as a continuous model. This characteristic will not be 
partially discontinuous. 
After executing the process of step 105, the computer 22 temporarily 
terminates the subsequent process. When "0.1 sec" passes after the current 
"learning control routine" has started, the computer 22 executes the 
processes of steps 101 to 105 again. 
Learning control using the neural network technology is carried out in this 
manner, which is to say that computer 22 learns the "acceleration model" 
produced as a result of changes in vehicular acceleration occasioned by 
the driver DR as well as through the "throttle sensitivity model". 
Accordingly, the "weighting coefficients" of synapses sp stored in the 
backup RAM 26 will be rewritten continuously as computer 22 continues to 
learn. 
The initial values of the "weighting coefficients" at the time of factory 
shipment of the vehicle 1 are determined as follows. With regard to the 
acceleration model, the vehicle 1 is driven by a plurality of drivers DR. 
The accelerations G obtained then are learned as "teaching data" and the 
averaged characteristics of the learned results become an initial value. 
The "weighting coefficients" for the throttle sensitivity model are so 
learned as to make the throttle sensitivity model output Thx "O". 
A description will now be given of the operations for the throttle angle 
control executed by the computer 21, based on the throttle sensitivity Thg 
as determined from both the above-described "learning control routine" and 
the acceleration stroke S. FIG. 9 shows a flowchart illustrating the 
"throttle angle control routine" in the throttle angle control program, 
which is run by the computer 21. This routine is executed repeatedly at a 
given time interval. 
After the elapse of a predetermined time following the initialization of 
the current "learning control routine", the computer 21 first reads the 
throttle angle Th based on the signal from the throttle sensor 9 in step 
201. The computer 21 also reads the latest throttle sensitivity Thg and 
acceleration stroke S output from the computer 22. Alternatively, the 
computer 21 reads the target throttle angle Thg.S output from the computer 
22. If a reading of the throttle sensitivity Thg and acceleration stroke S 
is preformed, the computer 21 multiplies Thg by S to obtain the target 
throttle angle Thg.S. 
In the next step 202, the computer 21 determines if the current throttle 
angle Th is smaller than the target throttle angle Thg.S. Following this 
operation, the computer 21 rotates the DC motor 8 forward to drive the 
throttle valve 7 in the opening direction in step 203. Subsequently, the 
computer 21 reads the throttle angle Th based on the signal from the 
throttle sensor 9 in step 204. 
In step 205, the computer 21 again determines if the throttle angle Th is 
smaller than the target throttle angle Thg.S. After this, the computer 21 
returns to step 203 and repeats the processes of steps 203, 204 and 205 to 
further drive the throttle valve 7 in the opening direction. If the 
decision condition in step 205 is not satisfied, the computer 21 
determines that it is unnecessary to further drive the throttle valve 7 in 
the opening direction and temporarily terminates the subsequent process. 
If the condition in step 202 is not satisfied, the computer 21 determines 
whether the throttle angle Th is larger than the target throttle angle 
Thg.S in step 206. Should angle Th be smaller than angle Thg.S, the 
computer 21 determines that the throttle angle Th matches with the target 
throttle angle Thg.S and temporarily terminates the subsequent process. 
If throttle angle Th is larger than target throttle angle Thg.S, the 
computer 21, in step 201, rotates the DC motor 8 backward to drive the 
throttle valve 7 in the closing direction. Subsequently, in step 208, the 
computer 21 reads the throttle angle Th based on the signal from the 
throttle sensor 9. 
In step 209, the computer 21 determines if the throttle angle Th is larger 
than the target throttle angle Thg.S. Should this condition be met, the 
computer 21 returns to step 207 and repeats the processes of steps 207, 
208 and 209 to further drive the throttle valve 7 in the closing 
direction. If the decision condition in step 209 is not satisfied, the 
computer 21 determines that it is unnecessary to further drive the 
throttle valve 7 in the closing direction and temporarily terminates the 
subsequent process. 
In this manner, the rotation of the DC motor 8 is controlled in such a way 
that the throttle angle Th matches the target throttle angle Thg.S and the 
angle of the throttle valve 7 is controlled accordingly. As a result, the 
amount of air flowing through the air intake passage 3 is adjusted and the 
output of the engine 2 is controlled. The driving power of the vehicle 1 
is controlled as a consequence. 
As described above, according to the first embodiment, at the time when 
throttle sensitivity Thg is learned, driver DR occasions a change in the 
acceleration of the vehicle which in turn results in an "acceleration 
model" being generated from acceleration G data. Based on the 
"acceleration model", the "throttle sensitivity model" is altered to 
determine the throttle sensitivity Thg. The target throttle angle Thg.S is 
in turn obtained by multiplying the determined throttle sensitivity Thg by 
the acceleration stroke S. The opening/closing of the throttle valve 7 is 
controlled in such a way that the value coincides with the throttle angle 
Th. 
In accordance with the above described conditions, the "acceleration model" 
always is obtained, and the "throttle sensitivity model" is obtained after 
the determination of the "acceleration model". The throttle angle Th of 
the engine 2 is always controlled corresponding to the change in 
acceleration G, which in turn is produced as a result of the changes in 
the vehicle's acceleration occasioned by driver DR. 
Large changes in the acceleration of the vehicle 1 produce corresponding 
increases in the throttle sensitivity Thg. These, in turn, reduce the 
range of changes in acceleration stroke S to a value corresponding to the 
acceleration G, thus allowing for a large acceleration G with a small 
amount of thrusting on the accelerator pedal 10. As a result, the driver 
DR will feel as if the accelerating performance of the vehicle 1 has been 
improved. For example, when the driver DR is in a hurry or is driving the 
vehicle 1 on a clear expressway without a traffic jam and thus wants to 
drive the vehicle 1 faster, a large acceleration G can be yielded by a 
little thrusting on the accelerator pedal 10, thus improving the feeling 
of acceleration. 
With a small change in the acceleration of the vehicle 1, the throttle 
sensitivity Thg decreases. This decrease produces a corresponding increase 
in the change made to the acceleration stroke S to a value that 
corresponds to the acceleration G, thus allowing for a fine variation in 
acceleration G even with a large thrust on the accelerator pedal 10. As a 
result, the operability of the accelerator pedal 10 by the driver DR will 
be improved. For example, when the driver DR is not in a hurry or is 
driving the vehicle 1 on a road under poor conditions, such as a traffic 
jam or snowy weather, and thus wants to drive the vehicle 1 slowly, the 
acceleration G can be kept to a minimum even with relatively large thrusts 
made to the accelerator pedal 10, thus improving the operability of the 
vehicle 1. 
In short, according to the first embodiment, since learning is performed in 
such a way as to meet the particular acceleration required by the driver 
DR, the throttle sensitivity Thg matching the driver's DR acceleration 
requirements is always determined. As a result, it is always possible to 
achieve driving power control of the vehicle 1 that matches with the 
characteristics of the driver DR, regardless of whether the driver DR is 
in a hurry, relaxed, etc., and irrespective of the driving environment 
(road conditions, day or night, inside a tunnel, rainy or snowy weather, 
mounting road, traffic jam, etc.). 
According to the first embodiment, since neural network technology is 
employed in the learning control of the computer 22, the whole 
relationship among the acceleration stroke S, the vehicle speed V and the 
acceleration model output Gx is learned as a continuous rather than a 
partially discontinuous model. Likewise, the whole relationship among the 
acceleration stroke S, the vehicle speed V and the throttle sensitivity 
model output Thx is learned as a continuous rather than a partially 
discontinuous model. 
The above is possible due to the use of the neural network technology 
interpolating the "acceleration model" learned as a result of the 
discontinuous points of the acceleration stroke S and vehicle speed V. 
That is, the correction of the acceleration model output Gx that is made 
for a specific range of the acceleration stroke S and the vehicle speed V 
corresponds to the correction of the acceleration model output Gx for 
another range of the acceleration stroke S and vehicle speed V. 
Furthermore, the correction of the throttle sensitivity model output Thx 
that is made within a specific range of the acceleration stroke S and the 
vehicle speed V corresponds to the correction of the throttle sensitivity 
model output Thx for another range of the acceleration stroke S and 
vehicle speed V. 
Accordingly, it is possible to continuously control the driving power of 
the vehicle 1 for the entire range of the vehicle speed V for the amount 
of the manipulation of the accelerator pedal 10 by the driver DR, or over 
the entire manipulation range of the acceleration stroke S. When the 
accelerator pedal 10 is continuously thrust downward, therefore, it is 
possible to prevent the acceleration G of the vehicle 1 from abruptly 
changing, thus ensuring a smooth increase in vehicle speed V. 
Furthermore, according to the first embodiment, the "acceleration model" is 
estimated from the acceleration G while the "throttle sensitivity model" 
is altered based on the result of the estimation. This eliminates the need 
for interpolation of a map and shortens the calculation time in contrast 
to the case where a map is redrawn by simple partial compensation 
(correction). 
In addition, according to the first embodiment, the acceleration sensor 12 
is connected to the I/O interface circuit 27 of the computer 22 via the 
low-pass filter 29. If external noise is introduced into the detection 
signal produced by acceleration sensor 12 during the movement of vehicle 1 
on a rough road, for example, a high-frequency component relative to that 
noise will be attenuated by the low-pass filter 29..That is, even when the 
signal reporting the acceleration G from the acceleration sensor 12 
contains a large amount of noise as shown in FIG. 10, that signal after 
passing the low-pass filter 29 is filtered to the signal of the 
acceleration G as shown in FIG. 11. 
Consequently, the computer 22 learns using acceleration G data that has 
unwanted noise removed. It is therefore possible for the computer 22 to 
learn the "acceleration model" and "throttle sensitivity model" without 
the influence of externally generated interference, and that, in turn, 
prevents the adjustment of the throttle sensitivity Thg in the wrong 
direction. 
Second Embodiment 
The second embodiment of the present invention will be described with 
reference to FIGS. 12 through 15. Those elements in the second embodiment 
that have substantially the same structure as those elements in the first 
embodiment will be given the same reference numerals to avoid repeating 
their descriptions. The following description will be centered on the 
specific differences between the first and second embodiments. 
In the second embodiment, as shown in FIG. 12, a model learning computer 41 
is used instead of the neuro computer 22 of the first embodiment. In this 
computer 41, a new method concerning the learning (updating) and execution 
(reading) of data maps is employed in place of the neural--network 
technology of the first embodiment. 
The electrical configuration of the computer 41 is substantially the same 
as that of the computer 22 in the first embodiment shown in FIG. 2. The 
computer 41 includes the CPU 23, ROM 24, RAM 25, backup RAM 26, I/O 
interface circuit 27 and bus 28, etc. 
A learning control program, or the like, which is associated with the new 
method concerning the learning (updating) and execution (reading) of data 
maps, is stored in advance in the ROM 24. The backup RAM 26 has two maps 
(to be described later) stored in advance. Based on the input values from 
the individual sensors 11 to 13, the CPU 23 executes a learning control of 
an "acceleration model", occasioned by changes made by the driver DR to 
the vehicle's acceleration, and a learning control of a "throttle 
sensitivity model" according to this "acceleration model", in accordance 
with the learning control program stored in the ROM 24. The CPU 23 sends 
out the learning results to the computer 21. 
Stored in advance in the ROM 31 of the computer 21 is a throttle angle 
control program, which controls the opening/closing of the throttle valve 
7 based on the learning results of the model learning computer 41. Based 
on the data of the learned results sent from the model learning computer 
41 and the signal from the throttle sensor 9, the CPU 30 properly controls 
the DC motor 8 in accordance with the aforementioned throttle angle 
control program. 
The new method concerning the learning (updating) and execution (reading) 
of data maps, which is adapted to the computer 41, will be discussed 
below. 
Stored in the backup RAM 26 are a map for "learning the acceleration model" 
shown in FIG. 13 and a map for "learning the throttle sensitivity model" 
shown in FIG. 14. These maps are prepared with the acceleration stroke S 
and vehicle speed V as parameters. 
Stored in the map in FIG. 13 are the results of learning the relationship 
among the acceleration stroke S, the vehicle speed V and the acceleration 
model output Gx. More specifically, the computer 41 uses the acceleration 
G, input from the acceleration sensor 12, as "teaching data". The computer 
41 compares the acceleration model output Gx, attained by referring to the 
map in FIG. 13, with the above-mentioned acceleration G. The computer 41 
treats the comparison result, an acceleration deviation .DELTA.G (=G-Gx), 
as an "error signal" and learns (updates) data in the map so as to reduce 
the error portion of this "error signal". 
That is, the computer 41 treats any particular level of acceleration G, 
required by the driver DR, as "teaching data". Using the map, the computer 
41 learns the relationship among the acceleration stroke S, the vehicle 
speed V and the acceleration G as the "acceleration model" required by the 
driver DR. This in effect reduces the deviations in the "teaching data". 
Then, the computer 41 accesses the learning results through the map as the 
acceleration model output Gx. 
That is, in the second embodiment like in the first embodiment, the 
"acceleration model" is learned in such a direction that the acceleration 
model output Gx approaches the acceleration G, as shown in FIG. 5. The 
learned results of driving data obtained by a plurality of drivers are 
stored as the initial values of the "acceleration model" in the 
aforementioned map. 
The results of learning the relationship among the acceleration stroke S, 
the vehicle speed V and the throttle sensitivity model output Thx are 
stored in the map "learning throttle sensitivity model" as shown in FIG. 
14. More specifically, the computer 41 uses the acceleration deviation 
.DELTA.G between the acceleration G and the acceleration model output Gx 
as an "error signal". The computer 41 alters the throttle sensitivity 
model output Thx, obtained by referring to the map in FIG. 14, based on 
the "error signal" and updates the data in the map to the altered value. 
In other words, using the map, the computer 41 learns the relationship 
among the acceleration stroke S, the vehicle speed V and the throttle 
angle Th as the "throttle sensitivity model" required by the driver's DR 
acceleration needs in order to decrease the error portion of the "error 
signal". The learning results are obtained as the throttle sensitivity 
model output Thx. That is, in the second embodiment as in the first, the 
"throttle sensitivity model" is learned as the characteristic as shown in 
FIG. 6. The reference throttle sensitivity ("1.0" in the second 
embodiment) is stored as the initial value of the "throttle sensitivity 
model" in the aforementioned map. 
A description will now be given regarding how maps may be used to learn the 
"acceleration model" and the "throttle sensitivity model". If typical maps 
are used to learn the aforementioned models, the acceleration stroke S and 
the vehicle speed V become unequal-interval data because the acceleration 
stroke S, vehicle speed V and acceleration G are input only every given 
time. Therefore, learning (updating) will not be performed evenly over the 
entire areas of the maps, and the map outputs become discontinuous. To 
ensure continuous map outputs, the learning (updating) and execution 
(reading) of the maps are carried out in the following manner in the 
second embodiment. 
FIG. 15 shows a map used for explaining this method. This map contains two 
variables, "X" and "Y", and the variable X is divided by M and the 
variable Y is divided by N, yielding a two-dimensional M * N matrix Z (M, 
N) where M and N are both integers. The minimum value for the variable X 
handled in this map is "XO" and the maximum value is "X1". The minimum 
value for the variable Y handled in this map is "Y0" and the maximum value 
is "Y1". The range of the address Ax in the direction of the variable X in 
the map is "0 to (M-1)" and the range of the address Ay in the direction 
of the variable Y in the map is "0 to (N-1)". 
The map address (Ax, Ay) of arbitrary (X, Y) is expressed by the following 
equations (2) and (3). 
EQU Ax=(X-X0)/Rx (2) 
EQU Ay=(Y-Y0)/Ry (3) 
Rx and Ry in the equations (2) and (3) indicate the range of (X, Y) 
represented by one point on the map, and are given by the following 
equations (4) and (5). 
EQU Rx=(X1-X0)/M (4) 
EQU Ry=(Y1-Y0)/N (5) 
Arbitrary (X, Y) are represented by the map address (Ax, Ay) in the 
equations (2) and (3). The distance Dxy between any given map address (Ax, 
Ay) and arbitrary (X, Y) is expressed by the following equations (6), (7) 
and (8). In equations (6) to (8), "Dx" is the distance in the direction of 
the variable X and "Dy" is the distance in the direction of the variable 
Y. 
EQU Dx=(X-X0)-Ax*Rx (6) 
EQU Dy=(Y-Y0)-Ay*Ry (7) 
EQU Dxy=(Dx.sup.2 +Dy.sup.2).sub.1/2 (8) 
When the distance Dxy in the equation (8) is smaller than an arbitrarily 
small positive distance Dmin, the distance Dxy is given by the following 
equation (9). 
EQU Dxy=Dmin (9) 
Given that the reciprocal of the distance Dxy between arbitrary (X, Y) and 
every point on the map is "Wxy" and the sum of the reciprocals Wxy is "W", 
those values are expressed by the following equations (10) and (11). 
##EQU1## 
The learning (updating) of the map is performed for all the points on the 
map using the following equation (12) in which ".epsilon." (small positive 
constant) is a learning ratio and ".sigma." is a learning signal for 
arbitrary (X, Y). 
EQU Z(Zx, AY)=Z(Ax, Ay)+.epsilon.*.sigma.Wxy/W (12) 
The execution (reading) of the map is done for all the points on the map 
using the following equation (13). In this equation, the product between 
the map value and a weighting coefficient proportional to the reciprocal 
of the distance, Wxy, is obtained. 
##EQU2## 
The learning (updating) and execution (reading) of the map are performed in 
the above manner. In the second embodiment, the acceleration model output 
Gx is assigned for the "acceleration model" and the throttle sensitivity 
model output Thx is assigned for the "throttle sensitivity model". In the 
second embodiment, the number of segmentations of the aforementioned map 
variables X and Y is "M=N=4", and the acceleration stroke S is assigned to 
the variable X while the vehicle speed V is assigned to the variable Y. 
The learning (updating) and execution (reading) of the map are carried out 
based on the learning control program, which is stored in advance in the 
ROM 24 of the computer 41. In the second embodiment, the learning control 
program ultimately obtains the relationship between the acceleration 
stroke S and the throttle sensitivity Thg as shown in FIG. 7. 
The basic flow of information processing for computer 41 is the same as 
that of the "learning control routine" in the first embodiment shown in 
FIG. 8. 
This "learning control routine" is executed to learn the characteristics of 
both the "acceleration model" required by the driver DR and the "throttle 
sensitivity model." Here, the characteristics of the "acceleration model" 
and the "throttle sensitivity model", which are intermittently processed, 
are rewritten and stored in the respective maps in the backup RAM 26. 
According to the second embodiment, the computer 21 executes the throttle 
angle control based on the throttle sensitivity Thg, determined by the 
aforementioned "learning control routine", and the acceleration stroke S. 
The basic flow of processing for the throttle angle control that is 
executed here is the same as that of the "throttle angle control routine" 
in the first embodiment shown in FIG. 9. This "throttle angle control 
routine" is executed to control the rotation of the DC motor 8 in such a 
way that the throttle angle Th matches with the target throttle angle 
Thg.S to control the angle of the throttle valve 7. As a result, the 
amount of air flowing through the air intake passage 3 is controllably 
adjusted. In this way, the output of the engine 2 is controlled, as is the 
driving power of the vehicle 1. 
As described above, according to the second embodiment, and as in the first 
embodiment, the acceleration requirements made by the driver DR are 
estimated as the "acceleration model" from the acceleration G. Based on 
the "acceleration model", the throttle sensitivity model" is altered to 
determine the throttle sensitivity Thg. The opening/closing of the 
throttle valve is controlled in such a way that the target throttle angle 
Thg.S, obtained by multiplying the determined throttle sensitivity Thg by 
the acceleration stroke S, coincides with the throttle angle Th. Moreover, 
in light of the acceleration requirements made by the driver DR, the 
"acceleration model" is always obtained, as is the "throttle sensitivity 
model". The throttle angle Th of the engine 2 is always controlled with 
the acceleration G that meets the request by the driver DR. 
According to the second embodiment, as in the first embodiment, since 
learning is performed in such a way as to meet the acceleration 
requirements of the driver DR, a corresponding throttle sensitivity is 
likewise always determined. As a result, it is always possible to control 
the driving power of the vehicle 1 according to the requirements and 
driving characteristic of the driver DR regardless of the driving 
environment. 
According to the second embodiment, the above-described new method 
concerning the learning (updating) and execution (reading) of the maps is 
employed in learning control by the computer 41. Therefore, the whole 
relationship among the acceleration stroke S, the vehicle speed V and the 
acceleration model output Gx is learned as a continuous model, rather than 
as a partially discontinuous model. Likewise, the whole relationship among 
the acceleration stroke S, the vehicle speed V and the throttle 
sensitivity model output Thx is learned as a continuous rather than a 
partially discontinuous model. 
This is because the above-described method for learning the maps is used. 
That is, when the "acceleration model" and the "throttle sensitivity 
model" are learned at arbitrary points of the acceleration stroke S and 
vehicle speed V, the values of all the other points on each map are 
updated based on the learning results. In other words, the correction of 
the acceleration model output Gx that is made for a specific range of the 
acceleration strokes S and the vehicle speeds V reflects on the correction 
of the acceleration model output Gx for another range of the acceleration 
stroke S and vehicle speed V. Furthermore, the correction of the throttle 
sensitivity model output Thx that is made within a specific range of the 
acceleration stroke S and the vehicle speed V reflects on the correction 
of the throttle sensitivity model output Thx for another range of the 
acceleration stroke S and vehicle speed V. 
Accordingly, it is possible to control the driving power of the vehicle 
continuously over the entire range of the acceleration stroke S. When the 
driver DR pushes the accelerator pedal 10 downward, the acceleration G of 
the vehicle 1 will not change abruptly, ensuring a smooth increase in 
vehicle speed V. This is the same advantage as obtained by the first 
embodiment where the neural network technology is employed. 
Using the method of learning maps in the second embodiment rather than that 
of the neural network technology in the first embodiment occasions the 
following advantages. In the second embodiment, the amount of computation 
becomes smaller than that required by the execution and learning of the 
neural network, thus making the amount of the contents of the operational 
program less than that needed by the neural network. The second embodiment 
can therefore shorten the computing time at the learning time by the 
reduced amount of computation, thus ensuring the reduction in the required 
memory capacity. This eliminates the need for a fast, large-capacity 
memory for the ROM 24, RAM 25, backup RAM 26 and so forth in the computer 
41 and contributes to reducing the manufacturing cost of the apparatus. 
The functions and advantages of the second embodiment described above are 
what should be in contrast with those of the first embodiment, and the 
other functions and advantages of the second embodiment are substantially 
the same as those of the first embodiment. 
Third Embodiment 
The third embodiment of the present invention will be described with 
reference to FIGS. 16 through 21. 
Those elements in the third embodiment which are substantially the same in 
structure as the elements in the first embodiment will be given the same 
reference numerals to avoid repeating their descriptions, and the 
following description will be centered on the specific differences. 
In the third embodiment, immediately after the vehicle's startup is 
detected and after the processing of the "throttle sensitivity model" is 
completed, the throttle sensitivity Thg is forcibly converged to a 
reference value substantially between a value suitable for the quick start 
of the vehicle and a value suitable for longer start. 
As shown in FIG. 16, a braking pedal 15 is provided next to the accelerator 
pedal 10 (the braking pedal 15 is shown above the accelerator pedal 10 in 
FIG. 16). The driver DR steps on the braking pedal 15 to achieve or 
maintain the breaked condition of vehicle 1. 
A push type stop lamp switch 14 is provided near the braking pedal 15. This 
switch 14 is normally in an off state, and it outputs an ON signal only 
when the driver DR depresses the braking pedal 15 a predetermined amount 
or more. The switch 14 is connected to the neuro computer 22. 
As shown in FIG. 17, in addition to the individual sensors 11 to 13, the 
switch 14 is connected to the I/O interface circuit 27. Various signals 
from the sensors 11-13 and the switch 14 are input to the CPU 23 via the 
I/O interface circuit 27. 
A learning control routine shown in FIG. 18 will be described below. The 
individual processes in this routine are executed using a counter to count 
the number of times the throttle sensitivity Thg is computed during the 
startup period of the vehicle 1. A count value .tau. is assigned to the 
result of this counting routine. 
The processes for learning the "acceleration model" and the "throttle 
sensitivity model" during the learning control routine are executed during 
the period (start sustain time) from the point where the following 
drive-start condition begins to the point where the drive-start condition 
ends. 
The drive start condition is considered satisfied when a drive start flag 
F1, which indicates whether or not the drive start condition is met, is 
"0", when a change .DELTA.S in acceleration stroke S per unit time (e.g., 
0.1 sec) is equal to or greater than a predetermined value (e.g., 3%), and 
when the vehicle speed V is equal to or less than a predetermined value 
(e.g., 5 Km/hour). The flag F1 is set to "1" when the drive start 
condition is met and to "0" when that condition is not met. 
The drive-start end condition is considered satisfied under the following 
three scenarios. First, where the drive start condition is met and the 
drive start flag F1 is set to "1", where a convergence start flag E is "0" 
and where the braking pedal 15 is pushed and the stop lamp switch 14 is 
set on. Second, the drive-start end condition is considered satisfied when 
a predetermined time (e.g., 1 sec) or longer elapses after the drive start 
condition is met, when the acceleration stroke S is equal to or less than 
a predetermined value (e.g., 10%), and when the vehicle speed V is equal 
to or less than a predetermined value (e.g., 10 Km/hour). And third, the 
drive-start end condition is considered satisfied when the count value 
.tau. reaches a preset value T (e.g., 75). 
The flag E indicates whether or not a process of converging (matching) the 
throttle sensitivity Thg to a reference value THG.sub.0 is started after 
the start action (to be described later) is completed. The flag E is set 
to "1" when the converging process starts or is in progress, and to "0" 
when the converging process has yet to be started. 
The count value .tau. and both flags F1 and E are set to "0" as initial 
values when the ignition key is operated to start the engine. When the 
control cycle of the learning control routine is 0.1 sec and the 
predetermined value T is 75, the start sustain time becomes 7.5 sec. 
The time chart shown in FIG. 21 shows a change in throttle sensitivity Thg 
obtained by the learning control routine when the vehicle 1 runs in the 
following manner. It is assumed that the vehicle 1 is stopped because of a 
red signal or some other reason before time t1. The time chart shows a 
case where a quick start is performed during a period between times t1 and 
t2, where constant speed cruising (driving other than the start action) is 
performed during a period between times t2 and t3, and where gentle 
starting and accelerating cruising is required by the driver DR during a 
period between times t3 and t4. 
When a predetermined time ("0.1 sec" in this case) passes following the 
beginning of the learning control routine, the computer 22 reads the 
acceleration stroke S, acceleration G and vehicle speed V based on various 
signals from the accelerator pedal sensor 11, acceleration sensor 12 and 
vehicle speed sensor 13 in step 301 in FIG. 18. From the acceleration 
stroke S, the computer 22 calculates the amount of its change .DELTA.S. 
The change .DELTA.S is the deviation in previously read and current values 
for the acceleration stroke S. 
Then, the computer 22 determines if the flag E is "1" in step 302. The 
computer 22 goes to step 304 when this decision condition is affirmative, 
and advances to step 303 upon reaching a negative result. 
In step 303, the computer 22 executes the learning of the "throttle 
sensitivity model". More specifically, the computer 22 acquires the 
throttle sensitivity model output Thx from the characteristic (see FIG. 6) 
of the already learned "throttle sensitivity model" based on the 
currently-read acceleration stroke S and vehicle speed V as input values. 
The computer 22 then determines the throttle sensitivity Thg according to 
the aforementioned equation (1). 
Next, the computer 22 sends the throttle sensitivity Thg and acceleration 
stroke S to the computer 21 in step 304. Alternatively the computer 22 
multiplies the throttle sensitivity Thg by the acceleration stroke S and 
sends the multiplication result as a target throttle angle Thg.S to the 
computer 21. 
In the next step 305, the computer 22 determines whether or not the drive 
start condition is met. When this decision condition is satisfied, the 
computer 22 moves to step 306 to set the flag F1 to "1" and resets the 
flag E to "0" and then proceeds to step 307. In the time chart in FIG. 21, 
considering that the drive start condition is satisfied at time t1, the 
computer 22 executes the processes of steps 305, 306 and 307 in that 
order. 
When the result at step 305 reflects a negative outcome, the computer 22 
proceeds to the operation at step 312 and determines if the flag F1 is 
"1". In the decision in step 305, the computer detects only the 
drive-start timing and does not consider whether or not the starting and 
accelerating cruising is in progress. If the drive start condition has 
already been met and the flag F1 is "1", which means the starting and 
accelerating cruising is in progress, the computer 22 proceeds from the 
operation at step 305 to the operation at step 312 and then to step 307. 
If a negative decision results at step 312, the computer 22 advances to 
step 313. 
When the process at step 307 is reached from the operations at step 306 or 
step 312, the computer 22 determines whether or not the drive-start end 
condition is met. If the process at step 307 is answered affirmatively, 
the computer 22 proceeds to step 313. 
If a negative result is occasioned at step 307, which indicates that 
starting or acceleration is in progress, the computer 22 moves to step 308 
to increment the count value .tau. by "1". 
Next, in steps 309 to 311, the computer 22 performs the same processes as 
those of steps 104 and 105 in FIG. 8. That is, the computer 22 learns the 
"acceleration model" required by the driver DR, using the acceleration G 
of the vehicle 1 as "teaching data" in step 309. More specifically, the 
computer 22 treats the acceleration G of the vehicle 1 detected by the 
acceleration sensor 12, as "teaching data". It then learns the 
relationship among the acceleration stroke S, the vehicle speed V and the 
acceleration G all of which is interpreted as the "acceleration model" in 
response to the acceleration demands occasioned by the driver DR. With 
this model,the computer is able to reduce data deviation in the "teaching 
data". 
Next, the computer 22 obtains the difference (G-Gx) between the 
acceleration G and the acceleration model output Gx which it defines as 
the acceleration derivation .DELTA.G. In step 311, the computer 22 
determines the "throttle sensitivity model" using that acceleration 
deviation .DELTA.G as an "error signal". In other words, using the 
acceleration deviation .DELTA.G as the "error signal", the computer 22 
establishes a relationship among the acceleration stroke S, the vehicle 
speed V and the throttle angle Th as the "throttle sensitivity model" in 
such a way as to reduce the error portion of the "error signal". 
After executing the process of step 311, the computer 22 temporarily 
terminates the subsequent processing. When "0.1 sec" passes after the 
learning control routine has started, the computer 22 executes the 
sequence of processes starting with step 301 again. 
Following a positive outcome at step 307 or when the outcome from step 312 
is negative, i.e., when the starting and accelerating cruising is not in 
progress, the computer 22 goes to step 313 to reset the flag F1 and the 
count value .tau. to "0". 
Next, the computer 22 determines if the throttle sensitivity Thg equals the 
predetermined reference value THG.sub.0 in step 314. Throttle sensitivity 
reference THG.sub.0 takes a value between the minimum value THG.sub.MIN 
(e.g., "0.5") and the maximum value THG.sub.MAX (e.g., "1.5"). In the 
third embodiment, "1.0" is set as the reference value THG.sub.0. 
When the decision condition in step 314 is met (Thg=THG.sub.0), the 
computer 22 temporarily terminates the subsequent processing. When "0.1 
sec" passes after the current learning control routine has started, the 
computer 22 executes the sequence of processes starting with step 301 
again. During the period between time t1 and time t2 in the time chart in 
FIG. 21, as the sequence of processes of steps 301 to 311 is repeated, the 
throttle sensitivity Thg varies (increases in this case) with time. 
When a positive outcome in step 314 is not reached (Thg.noteq.THG.sub.0), 
the computer 22 goes to step 315 to determine if the flag E is "1". When 
this condition is not met, i.e., when the flag E is "0", the computer 22 
judges that the drive-start end condition has been just satisfied, and 
sets the flag E to "1" in step 317. 
Next, the computer 22 computes the minimum amount of correction .DELTA.Thg 
for the throttle sensitivity Thg from the following equations (14) and 
(15) in step 318. 
EQU .DELTA.Thg=(THG.sub.0 -Thg)/n (14) 
##EQU3## 
In the equation (15), "i" is the number of adjustments of the throttle 
sensitivity, which is the number of times the learning control routine has 
been performed after the drive-start end condition has been met. The 
maximum value "L" that this adjustment number i takes is the number of 
times the learning control routine can be executed until the next drive 
start is initiated. 
Furthermore, "m.sub.i " is a compensation coefficient for changing the 
minimum amount of correction .DELTA.Thg, and is a function of the 
adjustment number i. This compensation coefficient m.sub.i is set 
previously to the characteristic as indicated by the solid line in the map 
in FIG. 19. More specifically, in this map, the compensation coefficient 
m.sub.i is set to "1" when the adjustment number i is "0" or "L" 
Coefficient m.sub.i takes the maximum value when the adjustment number i 
is "L/2". Then, the compensation coefficient m, slowly changes when the 
adjustment number i is approximately "0" and "L", and changes sharply when 
i takes the other values. 
It is to be noted that "n" obtained by the equation (15) takes a value, for 
example, "10". In this case, the minimum amount of correction .DELTA.Thg 
obtained from the equation (14) becomes a negative value. 
After computing the minimum amount of correction .DELTA.Thg in step 318, 
the computer 22 moves to step 319 to add .DELTA.Thg.m.sub.i to the current 
throttle sensitivity Thg and to set the result of the addition as a new 
throttle sensitivity Thg. Since the minimum amount of correction 
.DELTA.Thg is a negative value and the compensation coefficient m.sub.i is 
a positive value, the throttle sensitivity Thg is decreased by 
.DELTA.Thg.m.sub.i. 
After executing the process of step 319, the computer 22 temporarily 
terminates the subsequent processing. When "0.1 sec" passes after the 
current learning control routine has started, the computer 22 executes the 
sequence starting with step 301 again. 
As described above, the flag E is set to "1" in step 317. In the subsequent 
control cycle, therefore, the computer 22 judges that the decision 
condition in step 315 is met, and executes the process of step 316 instead 
of steps 317-319. The computer 22 at this time also judges whether the 
condition at step 302 is met. The computer 22 does not execute the process 
of step 303 (execution of the throttle sensitivity model) and will instead 
send the throttle sensitivity Thg, determined in step 319 or step 316, to 
the computer 21 until the flag E is set to "0" or until the next drive 
start is initiated. 
When the throttle sensitivity Thg is larger than the reference value 
THG.sub.0 at time t2 as shown in the time chart in FIG. 21, the computer 
22 judges that the condition set forth in step 314 is not satisfied. The 
computer 22 then repeats the process for converging the throttle 
sensitivity Thg to the reference value THG.sub.0 (updating of the throttle 
sensitivity Thg) in the following manner until that decision condition is 
satisfied. That is, the computer 22 moves from step 315 to 317, to 318, to 
319, to 301, to 302 and then to 304 immediately after the decision 
condition in step 314 is not met, and then moves from step 315 to step 
316, to 301, to 302 and then to 304 in the next control cycle. 
While the minimum amount of correction .DELTA.Thg is a constant during the 
execution of step 316 (step 319 immediately after the decision condition 
in step 314 is not met), the compensation coefficient m.sub.i varies as 
shown in FIG. 19 with an increase in the value of the adjustment number i, 
as the process of step 316 (step 319 immediately after the decision 
condition in step 314 is not met) is executed. Therefore, the throttle 
sensitivity Thg, which is updated in step 316 (step 319 immediately after 
the decision condition in step 314 is not met), gradually decreases after 
time t2 with the passage of time and eventually matches the reference 
value THG.sub.0, as indicated by the solid line in FIG. 20. More 
specifically, the throttle sensitivity Thg gently decreases immediately 
after time t2, and drastically decreases thereafter. The throttle 
sensitivity Thg also decreases the most drastically when the adjustment 
number i is L/2, and slowly decreases thereafter. 
When the process of step 316 (step 319 immediately after the decision 
condition in step 314 is not met) is performed L times and the throttle 
sensitivity Thg mirrors the reference value THG.sub.0 at time t2a, the 
computer 22 judges that the condition in step 314 is met and terminates 
the process of updating the throttle sensitivity Thg. The throttle 
sensitivity Thg is held at the reference value THG.sub.0 until the next 
drive start is initiated (timing t2 to t3). 
According to the learning control routine, as described above, when the 
starting of the vehicle 1 is detected and the learning of the acceleration 
model and of the throttle sensitivity model is complete, the output of 
that throttle sensitivity model, throttle sensitivity Thg, is forcibly 
converged to the reference value THG.sub.0. 
Even in the case after the throttle sensitivity Thg is adjusted to a low 
value due to a slow starting condition, and the driving state shifts to a 
constant cruising speed when the driver DR can request quick acceleration, 
the throttle sensitivity Thg will be forcibly converged to the reference 
value THG.sub.0 after the throttle sensitivity model is complete. 
For instance, given a case where the throttle sensitivity Thg, adjusted at 
the end of the drive start (timing t2), is held at a constant cruising 
speed (timings t2 to t3) from the point where the start action of the 
vehicle 1 is ended to the point where the next start action is initiated 
(see alternate long and two short dashes line) in FIG. 21, if the driver 
DR requests slow acceleration through the constant speed cruising, the 
accelerator pedal becomes too sensitive, making acceleration manipulation 
difficult. If, after the throttle sensitivity Thg is adjusted to a low 
value due to slow starting, the driving state is shifted to constant speed 
cruising during which the driver DR requests quick acceleration, the 
response of the accelerator pedal will be too slow and will also make 
acceleration manipulation difficult. 
According to the third embodiment, however, after the drive-start end 
condition of the vehicle 1 is satisfied and the learning of the throttle 
sensitivity model is completed, the throttle sensitivity Thg is forcibly 
converged to the predetermined reference value THG.sub.0. That is, the 
throttle sensitivity Thg is set back to the reference value THG.sub.0 
during the period (constant speed cruising) from the point where the start 
action of the vehicle 1 has ended to the point where the next learning of 
the throttle sensitivity model is executed. The next processing of the 
throttle sensitivity model is carried out based on this reference value 
THG.sub.0. 
The third embodiment therefore has the same functions and advantages as the 
first embodiment. In addition, in the case where the driver DR requests 
slow acceleration (see FIG. 21) during constant speed cruising after the 
throttle sensitivity Thg is adjusted to a high value, as well as where the 
driver DR requests quick acceleration, the throttle sensitivity Thg, used 
as reference data to control the driving of the DC motor 8, becomes the 
reference value THG.sub.0. Accordingly, the change in acceleration G as 
well as the amount of the manipulation of the accelerator pedal 10 becomes 
about the same. Since the reference value THG.sub.0 is set to a value 
between the throttle sensitivity Thg suitable for quick start of the 
vehicle 1 (maximum value THG.sub.MAX) and the throttle sensitivity Thg 
suitable for slower start of the vehicle 1 (minimum value THG.sub.MIN), 
the manipulation of the accelerator pedal 10 required to control the 
output of the engine 2 corresponds to any re-acceleration request by the 
driver DR and matches that re-acceleration request. 
In this manner, the influence of the adjustment of the throttle sensitivity 
Thg on the next learning of the throttle sensitivity model can be 
eliminated in constant speed cruising. 
Furthermore, according to the third embodiment, at the time the throttle 
sensitivity Thg is adjusted after the start action is complete, the 
throttle sensitivity Thg gradually and smoothly decreases with the passage 
of time and eventually converges to the reference value THG.sub.0, as 
shown in FIG. 20. While the driver DR may normally feel some pulling 
impact when the throttle sensitivity Thg sharply changes, the third 
embodiment of this invention will prevent the driver DR from feeling 
uncomfortable due to this impact. 
Fourth Embodiment 
The fourth embodiment of the present invention will now be described with 
reference to FIGS. 22 through 24. 
In the fourth embodiment, when it is determined that the vehicle 1 is at a 
constant cruising speed, the learning control is suppressed in favor of a 
preset value that is sent to computer 21 suitable for the constant 
cruising speed. The other structures are substantially the same as those 
of the first embodiment, and will be given the same reference numerals to 
avoid repeating their descriptions. The following description will be 
centered on the specific differences between the first and fourth 
embodiments. 
When a predetermined time ("0.1 sec" in this case) elapses from the start 
of a previous "learning control routine", the computer 22, in step 401 in 
FIG. 22, reads the acceleration stroke S, acceleration G and vehicle speed 
V based on various signals from the accelerator pedal sensor 11, 
acceleration sensor 12 and vehicle speed sensor 13. 
In the next step 402, the computer 22 calculates the amount of change 
.DELTA.S in acceleration stroke S, the change .DELTA.V in vehicle speed V 
and the change .DELTA.Gt in acceleration G. The magnitude with which 
.DELTA.S changes is the difference between the current value and the 
previously read value of the acceleration stroke S. Likewise, the 
magnitude with which .DELTA.V and .DELTA.Gt changes is the difference 
between the current values and the previously read values of the vehicle 
speed V and acceleration G respectively. 
Following step 402, the computer 22 determines in step 403 if learning is 
necessary to obtain the throttle sensitivity Thg, i.e., if the learning 
start condition is met. The learning start condition in the fourth 
embodiment is satisfied when the absolute value of the amount of change 
.DELTA.S and the vehicle speed V are greater than their respective 
arbitrary set values. 
When the decision at step 403 is met, the computer 22 sets a learning start 
flag F2 to "1" in step 404 and then moves to step 405. When the decision 
condition in step 403 is not met, the computer 22 advances directly to 
step 405 without executing the process of step 404. 
In step 405, the computer 22 determines whether or not the learning 
operation is progressing according to whether the following three 
conditions are met: first, the flag F2 is set to "1"; second, the count 
value T1 is determined to have a value from the beginning of the learning 
operation of less than a preset value; and third, the vehicle speed V and 
acceleration stroke S are greater than their respective arbitrary set 
values. 
When the learning operation is not in progress, the conditional at step 405 
is not met and the computer 22 advances to step 413. On the other hand, 
when the condition at step 405 is met, the computer 22 judges that 
learning operation is to continue, and advances to step 406. 
In step 406, the computer 22 increases the count value T1 by "1", and in 
steps 407 to 412 executes the same processes as those of steps 102 to 105 
in FIG. 8. First, in step 407, the computer 22 executes the learning of 
the "throttle sensitivity models." The computer 21 acquires the throttle 
sensitivity model output Thx from the "throttle sensitivity model" based 
on the currently-read acceleration stroke S and vehicle speed V as input 
values. At the next step 408, the computer 22 determines the throttle 
sensitivity Thg from the throttle sensitivity model output Thx by the 
aforementioned equation (1). 
The computer 22 sends the throttle sensitivity Thg and acceleration stroke 
S, to the computer 21 in step 409. Alternatively, the computer 22 
multiplies the throttle sensitivity Thg by the acceleration stroke S to 
obtain a target throttle angle Thg.S and sends the value to the computer 
21. 
Next, using the acceleration G of the vehicle 1 as "teaching data", the 
computer 22 learns the "acceleration model" requested by the driver DR in 
step 410. More specifically, using the acceleration G of the vehicle 1 as 
"teaching data", the computer 22 learns the relationship among the 
acceleration stroke S, the vehicle speed V and the acceleration G as the 
"acceleration model" required by the driver DR, and learns this 
relationship in such a way as to reduce the deviation contained in the 
"teaching data". 
Subsequently, the computer 22 obtains the deviation acceleration deviation 
.DELTA.G) between the acceleration G and the acceleration model output Gx 
at step 411. The computer 22 also learns the "throttle sensitivity model" 
using the acceleration deviation .DELTA.G as an "error signal" in step 
412. In other words, using the acceleration deviation .DELTA.G as the 
"error signal", the computer 22 learns the relationship among the 
acceleration stroke S, the vehicle speed V and the throttle angle Th as 
the "throttle sensitivity model" in such a way as to reduce the error 
portion of the "error signal". 
Learning control using the neural network technology is carried out in this 
manner, and the particular characteristics of the "acceleration model" 
required by the driver DR and the "throttle sensitivity model" are 
processed and learned. Here, the "weighting coefficients" of the synapses 
sp, as the characteristics of the "acceleration model" and "throttle 
sensitivity model", which are occasionally learned, are rewritten and 
stored in the backup RAM 26. 
After executing the process of step 412, the computer 22 temporarily 
terminates the subsequent process. When "0.1 sec" passes after the 
"learning control routine" has started, the computer 22 executes the 
sequence of processes starting with step 401 again. 
When the condition at step 405 is not satisfied, the computer 22 moves to 
step 413 to reset the flag F2 to "0" Next, the computer 22 resets the 
count value T1 since the beginning of the learning to "0" in step 414. 
Then, the computer 22 determines if the vehicle 1 is in "constant cruising 
speed" during which there is less of a change in the vehicle speed V than 
in step 415. In the fourth embodiment, a "constant cruising speed" is 
recognized when the combination of the absolute value of the amount of 
change .DELTA.S, the absolute value of the amount of change .DELTA.V and 
the absolute value of the amount of change .DELTA.Gt are smaller than 
their respective arbitrary set values. 
When the decision condition in step 415 is met, the computer 22, in step 
416, suppresses the learning operation and sends a separately preset value 
in place of the throttle sensitivity Thg or target throttle angle Thg.S. 
That is, the computer 22 sends the throttle sensitivity ThgC, separately 
set to a value suitable for a constant cruising speed, along with the 
acceleration stroke S to the computer 21. Alternatively, the computer 22 
multiplies the throttle sensitivity ThgC for the constant cruising speed 
by the acceleration stroke S to obtain a target throttle angle ThgC S and 
sends the value ThgC S to the computer 21. 
The "throttle sensitivity ThgC for a constant cruising speed" situation is 
set in accordance with the type of the vehicle 1, the characteristic of 
the engine 2 and so forth, and it is a predetermined constant value 
ranging from "0.8" to "1.0" and is stored in advance in the ROM 24. In the 
fourth embodiment, the throttle sensitivity ThgC for constant speed 
cruising is set to "0.9". 
After executing the process of step 416, the computer 22 temporarily 
terminates the subsequent process. When "0.1 sec" passes after the current 
"learning control routine" has started, and the computer 22 executes the 
sequence of processes starting with step 401 again. 
When the decision condition in step 415 is not met, the computer 22 moves 
to step 417 to execute the learning of the "throttle sensitivity model" as 
in step 407. More specifically, the computer 22 acquires the throttle 
sensitivity model output Thx from the characteristic of the already 
learned "throttle sensitivity model" based on the currently-read 
acceleration stroke S and vehicle speed V as input values. In the next 
step 418 as in step 408, the computer 22 determines the throttle 
sensitivity Thg from the throttle sensitivity model output Thx by the 
aforementioned equation (1). 
The computer 22 then sends the throttle sensitivity Thg and acceleration 
stroke S to the computer 21 in step 419 as in step 409. Alternatively, the 
computer 22 multiplies the throttle sensitivity Thg by the acceleration 
stroke S to obtain a target throttle angle Thg.S and sends the value Thg.S 
to the computer 21. 
After executing the process of step 419, the computer 22 temporarily 
terminates the subsequent process. When "0.1 sec" passes after the current 
"learning control routine" has started, the computer 22 executes the 
sequence of processes starting with step 401 again. 
The throttle sensitivity or the target throttle angle in "constant cruising 
speed" is set in this manner. 
Referring to FIG. 23, a description will now be given of the "throttle 
angle control routine" that is executed by the computer 21, based on the 
aforementioned throttle sensitivity Thg or throttle sensitivity ThgC for a 
constant cruising speed condition, and the acceleration stroke S. 
When a predetermined time passes after the previous "throttle angle control 
routine" has started, the computer 21 executes the process of step 501 
first. When executing this step, the throttle sensitivity Thg is to be 
learned and the computer 21 reads the throttle angle Th based on the 
signal from the throttle sensor 9. The computer 21 reads the latest 
throttle sensitivity Thg and acceleration stroke S or target throttle 
angle Thg.S, as output from the computer 22. If reading of the throttle 
sensitivity Thg and acceleration stroke S is the premise, the computer 21 
multiplies Thg by S to obtain the target throttle angle Thg.S. 
In executing step 501 when the vehicle 1 is in "constant speed cruising", 
in which the throttle sensitivity Thg is not learned, the computer 21 
reads the throttle angle Th based on the signal from the throttle sensor 
9. The computer 21 reads the latest throttle sensitivity ThgC and 
acceleration stroke S or target throttle angle Thg.S, output from the 
computer 22. If reading of the throttle sensitivity ThgC and acceleration 
stroke S is done, the computer 21 multiplies ThgC by S to obtain the 
target throttle angle Thg.S. 
In the next step 502, the computer 21 determines if the current throttle 
angle Th is smaller than the target throttle angle Thg.S. Alternatively, 
the computer 21 determines if the current throttle angle Th is smaller 
than the target throttle angle Thg.S. 
When this condition is met at step 502, the computer 21 rotates the DC 
motor 8 forward to drive the throttle valve 7 in the opening direction in 
step 503. Subsequently, the computer 21 reads the throttle angle Th based 
on the signal from the throttle sensor 9 in step 504. 
In step 505, the computer 21 determines again if the throttle angle Th is 
smaller than the target throttle angle Thg.S or ThgC.S. When this decision 
condition is met, the computer 21 returns to step 503 and repeats the 
processes of steps 503, 504 and 505 to further drive the throttle valve 7 
in the opening direction. If the decision condition in step 505 is not 
satisfied, the computer 21, in step 503, determines that it is unnecessary 
to further drive the throttle valve 7 open, and temporarily terminates the 
subsequent process. 
If the decision condition in the aforementioned step 502 is not satisfied, 
the computer 21, in step 506, determines if the current throttle angle Th 
is larger than the target throttle angle Thg.S or ThgC.S. When this 
condition is not met, the computer 21 determines that the throttle angle 
Th matches the target throttle angle Thg.S or ThgC.S, and just terminates 
the subsequent process temporarily. 
If the decision at step 506 is satisfied, the computer 21, in step 507, 
rotates the DC motor 8 backward to drive the throttle valve 7 in the 
closing direction. Subsequently, the computer 21, at step 508, reads the 
throttle angle Th based on the signal from the throttle sensor 9. 
In step 509, the computer 21 determines if the throttle angle Th is larger 
than the target throttle angle Thg.S or ThgC.S. When this conditional is 
met, the computer 21 returns to step 507 and repeats the processes of 
steps 507, 508 and 509 to further drive the throttle valve 7 in the 
closing direction. If the decision condition in step 509 is not satisfied, 
the computer 21 determines that it is unnecessary to further drive the 
throttle valve 7 in the closing direction, and temporarily terminates the 
subsequent processes. 
In this manner, the rotation of the DC motor 8 is controlled in such a way 
that the throttle angle Th matches the target throttle angle Thg.S or 
ThgC.S and the angle of the throttle valve 7 is controlled accordingly. As 
a result, the amount of air flowing through the air intake passage 3 is 
adjusted and the output of the engine 2 is controlled. The driving power 
of the vehicle 1 is controlled as a consequence. 
The fourth embodiment therefore has the same function and advantages as the 
first embodiment. In addition, according to the fourth embodiment, when 
the vehicle 1 is in "constant speed cruising", the learning operation 
performed to obtain the throttle sensitivity Thg is suppressed while the 
throttle sensitivity ThgC, preset in advance as a value suitable for a 
constant cruising speed condition, will be used. Further, the 
opening/closing of the throttle valve 7 is controlled in such a way that 
the target throttle angle ThgC.S, obtained by multiplying the throttle 
sensitivity ThgC by the acceleration stroke S, matches the throttle angle 
Th. 
When the vehicle shifts its driving state to a constant cruising speed 
condition from that of accelerated cruising, the angle of the throttle 
valve 7 is adjusted in accordance with the amount that the accelerator 
pedal 10 is manipulated. This throttle adjustment is based on the target 
throttle angle ThgC.S suitable for a constant cruising speed condition 
rather being based on the target throttle angle Thg.S learned at the 
acceleration time. 
Suppose that in FIG. 24 the throttle sensitivity Thg is finally determined 
to be a relatively high value of "1.5" through learning while the vehicle 
1 is in a starting and accelerating cruising condition. Also suppose that 
the vehicle 1 changes to constant speed cruising at time t10 after the 
starting and accelerating cruising. In this case, the high throttle 
sensitivity Thg, determined to be "1.5" through learning, is immediately 
changed to a relatively low throttle sensitivity ThgC of "0.9", which is 
suitable for a constant cruising speed condition, as indicated by the 
solid line in FIG. 24. Thereafter, this throttle sensitivity ThgC is 
maintained until the accelerating cruising starts again. This result is 
apparent when one compares it with the change in throttle sensitivity Thg 
in a comparative example as indicated by the broken line in FIG. 24. The 
angle of the throttle valve 7 will be adjusted on the basis of the target 
throttle angle ThgC.S obtained from that throttle sensitivity ThgC. 
By these means it is possible to improve the controllability of the 
accelerator pedal in order to reflect the intention of the driver DR 
regarding the control of the driving power of the vehicle 1 in constant 
speed cruising. More specifically, when the driver DR is requesting 
constant speed cruising, the throttle sensitivity is prevented from 
becoming unnecessarily high. This prevents the vehicle speed V from 
changing greatly in response to a slight change in acceleration stroke S. 
Thus when the vehicle 1 is in a constant cruising speed condition, an 
improved driving performance results, which, in turn, helps the overall 
ability of the driver to maintain proper driving control over the car. 
Fifth Embodiment 
The fifth embodiment of the present invention will be described with 
reference to FIGS. 25 through 30. In the fifth embodiment, when it is 
determined that the vehicle 1 is in turning, the learning control is 
released and the learning result obtained by the computer 22 is changed to 
a value separately set and suitable for a vehicle undergoing a turning 
movement. The other structures are substantially the same as those of the 
first embodiment, and will be given the same reference numerals to avoid 
repeating their descriptions. The following description will be centered 
on the specific differences between the first and fifth embodiments. 
As shown in FIG. 25, a steering wheel 16 is provided at the driver's seat 
in the vehicle 1 for steering both front wheels. The steering wheel 16 is 
fitted with a steering sensor 17 for detecting the steering angle 
.theta.S, the value of which is communicated to the neuro computer 22. 
As shown in FIG. 26, in addition to the individual sensors 11 to 13, the 
steering sensor 17 is connected to the I/O interface circuit 27 of the 
computer 22. Various signals from sensors 11-13 and 17 are input via the 
I/O interface circuit 27 to the CPU 23. 
FIG. 27 illustrates a learning control routine corresponding to what is 
shown in FIG. 8. When a predetermined time ("0.1 sec" in this case) has 
elapsed from the beginning of a previous learning control routine, the 
computer 22, in step 601, reads the steering angle .theta.S, acceleration 
stroke S, acceleration G and vehicle speed V based on various signals from 
the steering sensor 17, accelerator pedal sensor 11, acceleration sensor 
12 and vehicle speed sensor 13. 
In the next step 602, from the acceleration stroke S, the computer 22 
calculates the amount of change .DELTA.S in acceleration stroke S. The 
magnitude of change .DELTA.S detected is the difference between the 
current value and the previously read value of the acceleration stroke S. 
Then, in step 603, the computer 22 determines if learning is to be started 
to obtain the throttle sensitivity Thg, i.e., if the learning start 
condition is met. The learning start condition is the same as what has 
already been explained in the fourth embodiment, and it is satisfied when 
the absolute value of the amount of change .DELTA.S and the vehicle speed 
V are greater than their respective preset values. 
When the decision condition in step 603 is met, the computer 22 sets a 
learning start flag F2 to "1" in step 604 and then moves to step 605. When 
the condition 603 is not met, the computer 22 advances directly to step 
605 without executing the process of step 604. 
In step 605, the computer 22 determines whether or not the learning 
operation should continue based on three conditions: first, whether flag 
F2 is "1"; second, whether the count value T1, from a time when the 
learning operation was initialized, has a value smaller than a preset 
value; and third, whether the vehicle speed V and acceleration stroke S 
are greater than preset values. 
When the decision condition in step 605 is not met, the computer 22 judges 
that the learning operation is not to be carried out, and advances to step 
613. On the other hand, when this the learning operation is to be carried 
out, the computer 22 advances to step 606 where the count value T1 is 
increased by "1". Later, in steps 607 to 612, the computer 22 executes the 
same processes as those of steps 102 to 105 in FIG. 8. 
First, in step 607, the computer 22 acquires the throttle sensitivity model 
output Thx from the characteristic (see FIG. 7) of the previously learned 
"throttle sensitivity model" based on the current values read for the 
acceleration stroke S and vehicle speed V. In the next step 608, the 
computer 22 determines the throttle sensitivity Thg from the throttle 
sensitivity model output Thx by the aforementioned equation (1). 
The computer 22 sends the new values for throttle sensitivity Thg and 
acceleration stroke S to the computer 21 in step 609. Alternatively, the 
computer 22 multiplies the throttle sensitivity Thg by the acceleration 
stroke S to obtain a target throttle angle Thg.S and sends the value to 
the computer 21. 
Next, using the acceleration G of the vehicle 1 as "teaching data", the 
computer 22, in step 610, learns the "acceleration model" requested by the 
driver DR. More specifically, using the acceleration G of the vehicle 1, 
detected by the acceleration sensor 12, as "teaching data" to be compared, 
the computer 22 learns the relationship among the acceleration stroke S, 
the vehicle speed V and the acceleration G as the "acceleration model" 
required by the acceleration demands of driver DR. This learning is done 
in such a way as to reduce the deviation to the "teaching data". 
Subsequently, the computer 22, in step 611, obtains the deviation 
(acceleration deviation .DELTA.G) between the acceleration G and the 
acceleration model output Gx. Then, the computer 22, in step 612, learns 
the "throttle sensitivity model" using the acceleration deviation .DELTA.G 
as an "error signal". Thus, using the acceleration deviation .DELTA.G as 
the "error signal", the computer 22 learns the relationship among the 
acceleration stroke S, the vehicle speed V and the throttle angle Th as 
the "throttle sensitivity model" in such a way as to reduce the error 
portion of the "error signal". 
Learning control using the neural network technology is carried out in this 
manner by processing the characteristics of the "acceleration model" and 
the "throttle sensitivity model". During this operation, the "weighting 
coefficients" of the synapses sp as the characteristics of the 
"acceleration model" and "throttle sensitivity model" are rewritten and 
stored in the backup RAM 26. 
After executing the process of step 612, the computer 22 temporarily 
terminates any subsequent processing. When a certain period of time 
elapses, e.g., "0.1 sec", from when the current "learning control routine" 
had started, the computer 22 executes the sequence of processes starting 
with step 601 again. 
When the conditional at step 605 is not met, i.e., when the learning 
process is not continuing, the computer 22 moves to step 613 to reset the 
flag F2 to "0". Next, the computer 22 resets the count value T1 to "0" in 
step 614. 
Next, the computer 22 carries out in steps 615-619 the operations necessary 
for implementing the learning procedure during a "turning" motion of the 
vehicle. Specifically, the computer 22 determines first at step 615 if the 
vehicle 1 is "turning" as illustrated in the flowchart shown in FIG. 28. 
That is, in step 615a the computer 22 determines whether or not the 
absolute value of the currently read steering angle .theta.S is larger 
than an arbitrary set value .alpha.2. When this decision condition is met 
(.theta.S&gt;.alpha.2), the computer 22 moves to step 615b to increase the 
count value T2 of the counter by "1". 
In the next step 615c, the computer 22 determines whether or not the count 
value T2 is larger than an arbitrary set time .beta.1. When this decision 
condition is met (T2&gt;.beta.1), the computer 22 judges that the vehicle 1 
is "turning" because the steering angle .theta.S exceeds the set value 
.alpha.2 for a given time period and then moves to step 616. 
When the decision condition in step 615c is not satisfactory 
(T2.ltoreq..beta.1), the computer 22 judges that the vehicle 1 is not 
"turning" and moves to step 617. 
When the decision condition in step 615a is not satisfactory 
(.vertline..theta..vertline..ltoreq..alpha.2), the computer 22 judges that 
the vehicle 1 is not "turning" and resets the count value T2 to "0" in 
step 615d before moving to step 617. 
At step 616 in FIG. 27, this step having been reached from the 
aforementioned step 615c, the computer 22 suppresses the learning control 
operation and sends a separate preset value in place of the throttle 
sensitivity Thg or target throttle angle Thg.S, which would have been 
processed by the learning operation, to the computer 21. That is, the 
computer 22 sends the throttle sensitivity ThgT for turning, separately 
set and suitable for the turning of the vehicle 1, together with the 
acceleration stroke S to the computer 21. Alternatively, the computer 22 
multiplies the throttle sensitivity ThgT by the acceleration stroke S to 
obtain a target throttle angle ThgT.S and sends that value to the computer 
21. 
The "throttle sensitivity ThgT" is set in accordance with the type of the 
vehicle 1, the characteristic of the engine 2 and so forth, and it is a 
predetermined constant value ranging from "0.8" to "1.0" and stored in 
advance in the ROM 24. In the fifth embodiment, the throttle sensitivity 
ThgT is set to "0.9". 
After executing the process at step 616, the computer 22 temporarily 
terminates any subsequent processing. When "0.1 sec" elapses after the 
current "learning control routine" has started, the computer 22 executes 
the sequence of processing starting with step 601 once again. 
At step 617, the computer 22 executes the operation involved in learning 
the "throttle sensitivity model" as in step 607. More specifically, the 
computer 22 acquires the throttle sensitivity model output Thx from the 
previously learned "throttle sensitivity model" based on the current 
values for the acceleration stroke S and vehicle speed V as input values. 
In the next 618 as in step 608, the computer 22 determines the throttle 
sensitivity Thg from the throttle sensitivity model output Thx by the 
aforementioned equation (1). 
The computer 22, in step 619, then sends the current values for the 
throttle sensitivity Thg and acceleration stroke S to the computer 21, as 
it had in step 609. 
Alternatively, the computer 22 multiplies the throttle sensitivity Thg by 
the acceleration stroke S and obtains a target throttle angle Thg.S and 
sends that value to the computer 21. 
After executing the process at step 619, the computer 22 temporarily 
terminates any subsequent processing. Following the elapse of a certain 
period of time, e.g., "0.1 sec", from the beginning of the "learning 
control routine", the computer 22 executes the sequence of processing 
starting with step 601. 
Referring now to FIG. 29, a description will be given of the "throttle 
angle control routine" that is executed by the computer 21. 
When a predetermined time passed after the beginning of the previous 
throttle angle control routine, the computer 21 executes the process of 
step 701. In executing this process, where the throttle sensitivity Thg is 
to be learned, the computer 21 reads the throttle angle Th based on the 
signal from the throttle sensor 9. The computer 21 reads current values 
for the throttle sensitivity Thg and acceleration stroke S or target 
throttle angle Thg.S, which are output from the computer 22. If a pair of 
data, Thg and S are read, the computer 21 multiplies Thg by S to obtain 
the target throttle angle Thg.S. 
If step 701 is executed at a time when the vehicle 1 is in a "turning 
state" and if the throttle sensitivity Thg has not been learned, the 
computer 21 reads the throttle angle Th based on the signal from the 
throttle sensor 9. The computer 21 then reads the latest throttle 
sensitivity ThgT and acceleration stroke S or the target throttle angle 
ThgT.S, which is output from the computer 22. If the pair of data, ThgT 
and S, is read, the computer 21 multiplies ThgT by S to obtain the target 
throttle angle ThgT.S. 
In the next step 702, the computer 21 determines if the current throttle 
angle Th is smaller than the target throttle angle Thg.S or whether the 
current throttle angle Th is smaller than the target throttle angle Thg.S. 
Having made this decision, the computer 21 at step 703 drives the throttle 
valve 7 by rotating DC motor 8 forward in the open direction. 
Subsequently, the computer 21, in step 704, reads the throttle angle Th 
based on the signal from the throttle sensor 9. 
In step 705, the computer 21 redetermines whether the throttle angle Th is 
smaller than the target throttle angle Thg.S or if the throttle angle Th 
is smaller than the target throttle angle ThgT.S. Having made this 
determination, the computer 21 returns to step 703 and repeats the 
processes of steps 703 to 705 to further drive the throttle valve 7 in the 
opening direction. 
On the other hand, if the conditional in step 705 is not met, the computer 
21 determines that it is unnecessary to further drive the throttle valve 7 
open, and temporarily terminates any subsequent processing. 
If the conditional in the step 702 is not satisfied, the computer 21 moves 
to step 706 to determine if the current throttle angle Th is larger than 
the target throttle angle Thg.S. Alternatively, the computer 21 determines 
if the current throttle angle Th is larger than the target throttle angle 
ThgT.S. When this conditional is not met, the computer 21 determines that 
the throttle angle Th corresponds to the target throttle angle Thg.S or 
ThgT.S, and temporarily terminates any subsequent processing. 
If the conditional in step 706 is satisfied, the computer 21, in step 707, 
rotates the DC motor 8 backward to drive the throttle valve 7 toward a 
closed position. Next, in step 708, the computer 21 reads the throttle 
angle Th based on the signal from the throttle sensor 9. 
In step 709, the computer 21 determines again if the throttle angle Th is 
larger than the target throttle angle Thg.S or the target throttle angle 
ThgT.S. When this conditional is met, the computer 21 returns to step 707 
and repeats the processes of steps 707 to 709 to further drive the 
throttle valve 7 toward the closed position. If the conditional in step 
709 is not met, the computer 21 determines that it is unnecessary to 
further drive the throttle valve 7 closed and temporarily terminates any 
subsequent processing. 
In this manner, the rotation of the DC motor 8 is controlled in such a way 
that the throttle angle Th matches with the target throttle angle Thg.S or 
ThgT.S, and the angle of the throttle valve 7 is controlled accordingly. 
As a result, the amount of air flowing through the air intake passage 3 is 
adjusted and the output of the engine 2 is controlled. The driving power 
of the vehicle 1 is controlled as a consequence. 
The fifth embodiment therefore has the same function and advantages as the 
first embodiment. In addition, according to the fifth embodiment, when the 
vehicle 1 is in a "turning state", the operation for the computer 21 for 
learning the throttle sensitivity Thg is suppressed and the throttle 
sensitivity ThgT for turning will be used in place of the throttle 
sensitivity Thg. Furthermore, the opening/closing of the throttle valve 7 
is controlled in such a way that the set target throttle angle ThgT.S for 
turning, obtained by multiplying the throttle sensitivity ThgT by the 
acceleration stroke S, matches the throttle angle Th. 
When the vehicle 1 shifts its driving state to that of turning from that of 
accelerated straight cruising, the angle of the throttle valve 7 is 
adjusted in accordance with the amount of the manipulation of the 
accelerator pedal 10, based on the target throttle angle ThgT.C for 
turning instead of the target throttle angle Thg.S which is learned at the 
acceleration time. Such an adjustment of the throttle valve 7 will make 
the manipulation of pedal 10 more comfortable to the vehicle's driver. 
Suppose that in FIG. 30, the throttle sensitivity Thg is finally determined 
to be a relatively high value of "1.5" either while the vehicle 1 is in 
starting or in accelerating condition. Also suppose that at time t21 after 
starting the vehicle 1, the driver DR releases the accelerator pedal 10 
while turning the steering wheel 16. 
In this case, the high throttle sensitivity Thg, determined to be "1.5" 
through the computer's learning process, is changed during the period 
.beta.1 to a relatively low throttle sensitivity ThgT of "0.9" which is a 
setting suitable for turning at time t22. 
Thereafter, this throttle sensitivity ThgT for turning is maintained until 
the driver DR manipulates the accelerator pedal 10 and steering wheel 7 to 
positions indicative of straight line acceleration or deceleration. 
Even if learning is carried out by the driver DR stepping on the 
accelerator pedal 10 after time t23 and the throttle sensitivity Thg 
changes slightly, the throttle sensitivity Thg will not become 
unnecessarily high during a period from that time to a point at which the 
next large acceleration is made. This is apparent by comparison with a 
change in throttle sensitivity Thg of a comparative example indicated by 
the broken line in FIG. 30. The angle of the throttle valve 7 will be 
adjusted on the basis of the target throttle angle Thg.S, obtained from 
that throttle sensitivity ThgT, or on the basis of a target throttle angle 
close to the former throttle angle. 
That is, according to the fifth embodiment, when the vehicle 1 begins to 
execute a cornering motion after quick start, the throttle sensitivity is 
properly suppressed. Changes to vehicle speed V and acceleration G remain 
small with respect to the acceleration stroke S. Good maneuverability and 
operational performance can thus be maintained for the driver DR during 
vehicle cornering or turning. 
On the other hand, when the vehicle 1 makes one or any number of quick 
turns after gentle start, the throttle sensitivity Thg increases as does 
vehicle speed V and acceleration G with respect to the acceleration stroke 
S. Thus, in critical turning situations, for example, a relatively high 
sensitivity to the vehicle's acceleration may be maintained for good 
maneuverability and operational performance, both of which tend to reduce 
the weariness of the driver. 
Sixth Embodiment 
The sixth embodiment of the present invention will be described with 
reference to FIGS. 31 through 36. 
In the sixth embodiment, when the vehicle 1 is in "reverse", "park" or 
"stop", the learning control operations are suppressed and the learning 
result is provided to the computer using a preset value. The other 
structures of the present embodiment are substantially similar to that of 
the first embodiment, and will be given the same reference numerals to 
avoid repeating their descriptions. The following description will focus 
on the specific differences between the first and the sixth embodiments. 
As shown in FIG. 31, an automatic transmission is driveably coupled to the 
engine 2 with shift lever 18 provided near the driver's seat for changing 
the shift position SP of the transmission. In the sixth embodiment, the 
shift position SP can be changed from the "parking range (P)", "reverse 
range (R)", "neutral range (N)", "drive range (D)" or the like by the 
operation of the shift lever 18. The "parking range (P)" is used for a 
parked vehicle 1, the "reverse range (R)" for operating the vehicle in a 
reversed direction, and the "neutral range (N)" is used to allow the 
vehicle's 1 engine to turn while the vehicle 1 is stopped. The other 
positions including "drive range (D)" are used to drive the vehicle 1 
forward. 
Provided near the shift lever 18 is a shift position sensor 19 for 
detecting each shift position SP that is switched by the operation of the 
shift lever 18. The shift position sensor 19 is connected to the neuro 
computer 22. 
As shown in FIG. 32, the shift position sensor 19 as well as the 
aforementioned individual sensors 11 to 13 are connected via the I/O 
interface circuit 27 to the computer 22. Various signals from those 
sensors 11-13 and 19 are input via the I/O interface circuit 27 to the CPU 
23. 
FIGS. 33 and 34 illustrate a learning control routine corresponding to what 
is shown in FIG. 8. After the elapse of a predetermined time ("0.1 sec" in 
this case) from the beginning of the previous learning control routine, 
the computer 22 at step 801 reads the acceleration stroke S, acceleration 
G, vehicle speed V and shift position SP based on various signals from the 
accelerator pedal sensor 11, acceleration sensor 12, vehicle speed sensor 
13 and shift position sensor 19. 
In the next step 802, the computer 22 calculates the amount of change 
.DELTA.S in acceleration stroke S, the amount of change .DELTA.V in 
vehicle speed V and the amount of change .DELTA.Gt in acceleration G. The 
amount of change .DELTA.S is the difference between the current value and 
the previously read value of the acceleration stroke S. Likewise, the 
amount of change .DELTA.V and .DELTA.G represent the difference between 
the current and previously read values of the vehicle speed V and the 
acceleration G respectively. 
In the next step 803, the computer 22 determines if the currently read 
shift position SP is the "neutral range (N)" or "parking range (P)". If 
this condition is met, the computer 22 determines that the vehicle 1 is 
stopped or parked and moves to step 816. 
When the conditional at step 803 is not met, the computer 22 determines if 
the shift position SP is the "reverse range (R)" in step 804. When this 
conditional is met at step 804, the computer 22 determines that the 
vehicle 1 is in "reverse" and moves to step 815. 
On the other hand, when the conditional at step 804 is not met, the 
computer 22 judges that the shift position SP is the "drive range (D)" or 
the like for moving the vehicle 1 forward, and moves to step 805. There, 
the computer 22 determines if the operational learning process is to be 
started in order to obtain the throttle sensitivity Thg. The learning 
start condition in the sixth embodiment is satisfied when the absolute 
value of the amount of change .DELTA.S in acceleration stroke S and the 
vehicle speed V are greater than their respective arbitrary set values. 
When the conditional in step 805 is met, the computer 22 sets a learning 
start flag F2 to "1" in step 806 and then moves to step 807. On the other 
hand, when the decision condition in step 805 is not met, the computer 22 
advances directly to step 807 without executing the process of step 806. 
In step 807, the computer 22 determines whether or not the operational 
learning routine continues. In the sixth embodiment, it is judged whether 
the learning routine should continue when the following three conditions 
are met: when the flag F2 is "1", when the count value of T1 (to be 
described later) is smaller than an arbitrary predetermined value judged 
from a time at the beginning of the learning routine, and when the vehicle 
speed V and acceleration stroke S are greater than the respective 
arbitrary set values. 
When the conditional in step 807 is not met, the computer 22 judges that no 
learning routine is to be carried out and advances to step 820. 
Alternatively, when the conditional at step 807 is met, the computer 22 
judges that the learning routine is to be carried out and advances to step 
808, where the computer 22 increases the count value T1 by "1". 
Then, in steps 809 to 814, the computer 22 executes the same processes as 
those of steps 102 to 105 in FIG. 8. 
In step 809, the computer 22 executes the learning of the "throttle 
sensitivity model". More specifically, the computer 22 acquires the 
throttle sensitivity model output Thx from the previously learned 
"throttle sensitivity model" (see FIG. 6) based on the current values for 
the acceleration stroke S and vehicle speed V. In the next step 810, the 
computer 22 determines the throttle sensitivity Thg from the throttle 
sensitivity model output Thx using the aforementioned equation (1). 
The computer 22, in step 811, sends the current values of the throttle 
sensitivity Thg and acceleration stroke S to the computer 21. 
Alternatively, the computer 22 multiplies the throttle sensitivity Thg by 
the acceleration stroke S in order to obtain a target throttle angle 
Thg.S. 
Next, using the acceleration G of the vehicle 1 as "teaching data", the 
computer 22, in step 812, learns the "acceleration model" requested by the 
driver DR and in turn uses this information to reduce the deviation of the 
data comprising the "teaching data". 
Subsequently, the computer 22 in step 813 obtains the acceleration 
deviation .DELTA.G as between the acceleration G and the acceleration 
model output Gx. 
The computer 22 also learns the "throttle sensitivity model" using the 
acceleration deviation .DELTA.G as an "error signal" in step 814 by 
learning the relationship among the acceleration stroke S, the vehicle 
speed V and the throttle angle Th as the "throttle sensitivity model". 
Moreover, the computer 22 learns the characteristics of both the 
"acceleration model" required by acceleration demands made by the driver 
DR and the "throttle sensitivity model" in this manner. It is at this 
point that the computer 22 rewrites the "weighting coefficients" of the 
synapses sp stored in the backup RAM 26. 
After executing the process of step 814, the computer 22 temporarily 
terminates any subsequent processing. When "0.1 sec" passes after the 
"learning control routine" has started, the computer 22 executes the 
sequence of processes starting with step 801. 
When the conditional in step 807 is not met, the computer 22 moves to step 
820 and resets the learning start flag F2 to "0". Next, the computer 22 
resets the count value T1 to "0" in step 821. 
Subsequently, the computer 22, in step 822, begins the routine of learning 
the "throttle sensitivity model", as in step 809. More specifically, the 
computer 22 acquires the throttle sensitivity model output Thx from the 
characteristic of the already learned "throttle sensitivity model" based 
on the current values of the acceleration stroke S and vehicle speed V. In 
the next step 823, as in step 810, the computer 22 determines the throttle 
sensitivity Thg from the throttle sensitivity model output Thx from the 
aforementioned equation (1). 
The computer 22 then in step 824 sends the values for the throttle 
sensitivity Thg and acceleration stroke S to the computer 21, as in step 
811. Alternatively, the computer 22 multiplies the throttle sensitivity 
Thg by the acceleration stroke S to obtain a target throttle angle Thg.S 
and sends the value Thg.S to the computer 21. 
After executing the process of step 824, the computer 22 temporarily 
terminates any subsequent processing. Following the elapse of a certain 
period of time, e.g. "0.1 sec" from the time when the learning routine 
began, the computer 22 executes the sequence of processes starting with 
step 801. 
When the vehicle 1 is in "stop" or "park" condition and the conditional at 
step 803 is met, the computer 22 moves to step 816 to determine if the 
current value obtained for .DELTA.Gt is larger than an arbitrary set value 
.alpha.3 determined in advance. Alternatively, computer 22 determines 
whether the amount of change .DELTA.V in vehicle speed is larger than an 
arbitrary set value .beta.2 determined in advance. When either conditional 
is not met (.DELTA.Gt.ltoreq..alpha.3, or .DELTA.V.ltoreq..beta.2), the 
computer 22 in step 817 sets the throttle sensitivity Thg, obtained from 
the last learning routine, as the throttle sensitivity ThgN. 
However, when the conditional in step 816 is met (.DELTA.Gt&gt;.alpha.3, or 
.DELTA.V&gt;.beta.2), the computer 22, in step 818, sets the throttle 
sensitivity ThgN to "1.0". 
In step 819, arrived at from step 817 or 818, the computer 22 sends the 
currently set throttle sensitivity ThgN and acceleration stroke S to the 
computer 21. Alternatively, the computer 22 multiplies the throttle 
sensitivity ThgN by the acceleration stroke S to obtain the target 
throttle angle ThgN.S and sends that value to the computer 21. 
After executing the process of step 819, the computer 22 temporarily 
terminates the subsequent processing. When "0.1 sec" elapses from a time 
when the "learning control routine" previously began, the computer 22 
executes the sequence of processes starting with step 801 again. 
The throttle sensitivity or the target throttle angle when the vehicle 1 is 
in a "stop" or "park" condition will be set in the above manner. 
When the vehicle 1 is in "reverse" range and the conditional in step 804 is 
met, the computer 22 in step 815 suppresses the learning routine of the 
throttle sensitivity Thg. Next, the computer 22 writes a separately set 
value of the throttle sensitivity Thg or the target throttle angle Thg.S 
to the computer 21. That is, the computer 22 sends both the throttle 
sensitivity ThgR, a separately set value suitable for when the vehicle 1 
is in a reverse condition, and the acceleration stroke S to the computer 
21. Alternatively, the computer 22 multiplies the throttle sensitivity 
ThgR by the acceleration stroke S to obtain a target throttle angle ThgR.S 
and sends that value to the computer 21. 
The "throttle sensitivity ThgR is set in accordance with the type of the 
vehicle 1, the characteristic of the engine 2 and so forth, and it is a 
constant value determined within a range from "0.8" to "1.0" and stored in 
advance in the ROM 24. In the sixth embodiment, the throttle sensitivity 
ThgR is set to "0.9". 
After executing the process of step 815, the computer 22 temporarily 
terminates the subsequent processes. When "0.1 sec" passes after the 
current "learning control routine" has started, the computer 22 executes 
the sequence of processes starting with step 801. 
The throttle sensitivity or target throttle angle for the "reverse state" 
of the vehicle 1 is likewise set in this manner. 
FIG. 35 illustrates the throttle angle control routine executed by the 
computer 21. When a predetermined time passes after the beginning of the 
previous throttle angle control routine, the computer 21 executes the 
process at step 901. In executing this process, upon beginning to learn 
the throttle sensitivity Thg, the computer 21 reads the throttle angle Th 
based on the signal from the throttle sensor 9. The computer 21 reads the 
latest throttle sensitivity Thg and acceleration stroke S or the target 
throttle angle Thg.S, which is output from the computer 22. The computer 
21 multiplies Thg by S to obtain the target-throttle angle Thg.S. 
If, while executing step 901, the computer 21 determines that the vehicle 1 
is in "reverse" at a time when the throttle sensitivity Thg has not been 
learned, the computer 21 reads the throttle angle Th based on the signal 
from the throttle sensor 9. The computer 21 reads the latest throttle 
sensitivity ThgR and acceleration stroke S or the target throttle angle 
ThgR.S, which is output from the computer 22. In the case where data of 
the throttle sensitivity ThgR and acceleration stroke S is available as 
input, the computer 21 multiplies ThgR by S to obtain the target throttle 
angle ThgR.S. 
If, while step 901 is being executed, the vehicle 1 is in a "stopped" or 
"parked" condition at a time when the throttle sensitivity Thg has not 
been learned, the computer 21 reads the throttle angle Th based on the 
signal from the throttle sensor 9. The computer 21 reads the latest 
throttle sensitivity ThgN and acceleration stroke S or the target throttle 
angle ThgN.S, which is output from the computer 22. In the case where data 
of the throttle sensitivity ThgN and acceleration stroke S are available 
as input, the computer 21 multiplies ThgN by S to obtain the target 
throttle angle ThgN.S. 
In the next step 902, the computer 21 determines if the current throttle 
angle Th is smaller than the target throttle angle Thg.S. Alternatively, 
the computer 21 determines if the current throttle angle Th is smaller 
than the target throttle angle ThgR.S or ThgN.S. 
When this conditional is met, the computer 21, in step 903, rotates the DC 
motor 8 forward to drive the throttle valve 7 in the opening direction. 
Subsequently, the computer 21, in step 904, reads the throttle angle Th 
based on the signal from the throttle sensor 9. 
In step 905, the computer 21 determines whether the throttle angle Th is 
smaller than the target throttle angle Thg.S, ThgR.S or ThgN.S. When this 
decision condition is met, the computer 21 returns to step 903 and repeats 
the processes of steps 903 to 905 to further drive the throttle valve 7 
open. 
If the decision condition in step 905 is not satisfied, the computer 21 
determines that it is unnecessary to further drive the throttle valve 7 in 
the open direction and temporarily terminates the subsequent processing. 
If the conditional in the aforementioned step 902 is not satisfied, the 
computer 21, in step 906, determines if the current throttle angle Th is 
larger than the target throttle angle Thg.S, ThgR.S or ThgN.S. The 
computer 21 then determines that the throttle angle Th matches the target 
throttle angle Thg.S, ThgR.S or ThgN.S, and temporarily terminates the 
subsequent processes. 
If the decision condition in step 906 is satisfied, the computer 21 in step 
907 rotates the DC motor 8 backward to drive the throttle valve 7 toward 
the closed position. Subsequently, the computer 21, in step 908, reads the 
throttle angle Th based on the signal from the throttle sensor 9. 
In step 909, the computer 21 determines if the throttle angle Th is larger 
than the target throttle angle Thg.S, ThgR.S or ThgN.S. 
When this conditional is met, the computer 21 returns to step 907 and 
repeats the processes of steps 907 to 909 to further drive the throttle 
valve 7 closed. Alternatively, if the conditional in step 909 is not 
satisfied, the computer 21 determines that it is unnecessary to further 
drive the throttle valve 7 closed and temporarily terminates the 
subsequent processes. 
In this manner, the rotation of the DC motor 8 is controlled in such a way 
that the throttle angle Th matches with the target throttle angle Thg.S, 
ThgR.S or ThgN.S, and the angle of the throttle valve 7 is controlled 
accordingly. As a result, the amount of air flowing through the air intake 
passage 3 is adjusted and the output of the engine 2 is controlled. The 
driving power of the vehicle 1 is controlled as a consequence. 
The sixth embodiment therefore has the same function and advantages as the 
first embodiment. In addition, according to the sixth embodiment, when the 
vehicle 1 is in "reverse", in which the shift position SP of the shift 
lever 14 is at the "reverse range (R)", the operational learning routine 
to obtain the throttle sensitivity Thg is suppressed and the throttle 
sensitivity ThgR suitable for reverse will be provided. Furthermore, the 
opening/closing of the throttle valve 7 is controlled in such a way that 
the target throttle angle ThgR.S, obtained by multiplying the throttle 
sensitivity ThgR by the acceleration stroke S, matches the value for the 
throttle angle Th. 
When the vehicle 1 is in "reverse", therefore, the angle of the throttle 
valve 7 is adjusted in accordance with the amount of the manipulation of 
the accelerator pedal 10, based on the target throttle angle ThgR.C rather 
than the target throttle angle Thg.S learned at the time of forward 
acceleration. When the vehicle 1 moves backward, therefore, the 
manipulation of the accelerator pedal 10 needed to control the output of 
the engine 2 is easily maintained by the driver DR. 
Suppose that in FIG. 36, the throttle sensitivity Thg is finally determined 
to be a relatively high value of "1.5" while the vehicle 1 is in either a 
starting and forward accelerating mode. Also suppose that the vehicle 1 
stops at time t31 after the starting and accelerating cruising and changes 
to the reverse state at time t32. In this case, when the vehicle 1 comes 
to the reverse state, the high throttle sensitivity Thg, determined to be 
"1.5", is immediately changed to a relatively low throttle sensitivity 
ThgR of "0.9", as indicated by the solid line in FIG. 36. Thereafter, this 
throttle sensitivity ThgR is maintained until the forward acceleration 
starts again. This is apparent by comparison with the change in throttle 
sensitivity Thg of the comparative example indicated by the broken line in 
FIG. 36. The angle of the throttle valve 7 will be adjusted on the basis 
of the target throttle angle ThgR.S obtained from that throttle 
sensitivity ThgR. 
Accordingly, at the time the vehicle 1 moves backward, the control of the 
accelerator pedal can be improved in order to reflect the driving 
intention of the driver DR. More specifically, when the movement of the 
vehicle 1 is forward and next the driver DR requires a reverse movement, 
throttle sensitivity is kept from becoming unnecessarily high, thereby 
preventing the vehicle speed V from undergoing a dangerously rapid change. 
When the vehicle 1 is slowly started forward, on the other hand, the 
throttle sensitivity Thg decreases and changes in vehicle speed V and 
acceleration G with respect to the acceleration stroke S become smaller. 
Should the vehicle 1 then begin to travel in the reverse direction 
immediately after the gentle starting, the throttle sensitivity Thg will 
be immediately changed to the throttle sensitivity suitable for reverse 
ThgR. In the case where the driver DR is requesting the reverse motion, 
therefore, the throttle sensitivity is prevented from becoming 
unnecessarily low. Where the driver DR may wish to move the vehicle 1 
quickly in this case, a response to the acceleration of the vehicle 1 is 
kept from becoming too slow against the intention of the driver DR. In 
other words, when the driver DR wishes to move the vehicle 1 backward 
immediately after gentle forward driving has started, good operational 
performance suitable for reverse will be available. 
Further, in the sixth embodiment, since the learning of the throttle 
sensitivity Thg is suppressed when the vehicle 1 moves backward, the 
learning of the throttle sensitivity Thg will not be executed with a 
negative acceleration G. Thus, the throttle sensitivity will not be too 
low. 
In addition, according to the sixth embodiment, when the vehicle is in a 
"stop" or "park" condition when the shift position SP is at the "neutral 
range (N)" or "parking range (P)", the operational learning of the 
throttle sensitivity Thg is suppressed in favor of the preset value of 
"1.0" chosen as the throttle sensitivity ThgN for the stopped condition. 
The opening/closing of the throttle valve 7 is controlled in such a way 
that the target throttle angle ThgN.S, obtained by multiplying the 
throttle sensitivity ThgN by the acceleration stroke S, matches the 
throttle angle Th. 
Given engine racing conditions with the vehicle 1 in a "stop" or "park" 
condition, operational learning of the throttle sensitivity Thg may be 
suppressed. Even if the shift lever 18 is set to the "neutral range (N)" 
from "drive range (D)" while driving the vehicle 1, the throttle 
sensitivity Thg, which has ben obtained by the previous learning, will be 
used unless the amount of change .DELTA.Gt or .DELTA.V becomes larger by 
some degree. The throttle sensitivity will not therefore be changed 
unexpectedly, thus preventing the vehicle speed V from changing greatly to 
increase the motion of the vehicle 1. 
Seventh Embodiment 
The seventh embodiment of the present invention will now be described with 
reference to FIGS. 37 and 38. 
Since the schematic structure of the driving power control apparatus for 
the vehicle 1 in the seventh embodiment is the same as that of the third 
embodiment (see FIGS. 16 and 17), and since the throttle angle control 
routine executed by the computer 21 in the seventh embodiment is the same 
as that of the first embodiment (see FIG. 9), the schematic structure and 
the throttle angle control routine will not be discussed again. The 
specific learning control routine processed by the computer 22 in the 
seventh embodiment differs from that of the first embodiment in the 
following ways. 
The throttle sensitivity model learned by the computer 22 for any current 
starting action is disregarded upon the occurrence of the following two 
conditions: when the duration of the vehicle's starting action is shorter 
than a predetermined time, and when the throttle sensitivity model output 
Thg at the end of the vehicle's starting action is smaller than a 
predetermined value. Under these conditions, the learning states of the 
acceleration and throttle sensitivity models are reset to the time when 
the previous vehicle starting was completed 
Processing of the learning control routine of the computer 22, as shown in 
the flow chart of FIG. 37, is controlled by a program counter wherein the 
counter counts the number of times the throttle sensitivity Thg has been 
computed from the beginning of the starting action of vehicle 1 until a 
time when the starting action is complete. A count value .tau. is utilized 
as a value equivalent to the detected period of time during which the 
starting action of vehicle 1 is completed (start sustain time). The 
acceleration and throttle sensitivity models are processed and learned by 
the computer 22 during this time. 
The drive start condition is considered satisfied under the following 
conditions: when a drive start flag F1, indicative of the drive start 
condition, is set to zero; when the change in the acceleration stroke 
.DELTA.S per unit time (e.g., 0.1 sec) is equal or greater to a 
predetermined value (e.g., 3%); and when the vehicle speed V is equal to 
or less than a predetermined value (e.g., 5 Km/hour). Drive start flag F1 
is reset to "1" when the drive start condition is satisfied and to "0" 
when that condition is absent. 
The end of the drive start condition is met when the drive start condition 
is met, the drive start flag F1 is set to "1", the braking pedal 15 is 
thrust downward, and the stop lamp switch 14 is set on. Alternatively, the 
end of the drive start condition is considered as having been met when a 
predetermined time (e.g., 1 sec) has elapsed from a time after the drive 
start condition has been met, when the acceleration stroke S is equal to 
or less than a predetermined value (e.g., 10%), and when the vehicle speed 
V is equal to or less than a predetermined value (e.g., 10 Km/hour). The 
drive-start end condition is also considered satisfied under a condition 
when the count value .tau. becomes equal to or more than a first 
predetermined value T set previously. 
The count value .tau. and the drive start flag F1 are set to "0" as initial 
values when the ignition key is operated to start the engine. 
When a predetermined time ("0.1 sec" in this case) elapses from the 
previous starting of the learning control routine, the computer 22, in 
step 1001, reads the acceleration stroke S, acceleration G and vehicle 
speed V based on various signals from the accelerator pedal sensor 11, 
acceleration sensor 12 and vehicle speed sensor 13. 
Then, the computer 22 calculates the amount of change .DELTA.S in the 
acceleration stroke S. The change .DELTA.S is a deviation between the 
current and previous read values of the acceleration stroke S. 
In step 1003, the computer 22 executes the learning of the "throttle 
sensitivity model" by acquiring the throttle sensitivity model output Thx 
from (see FIG. 6) the previously learned "throttle sensitivity model" 
based on the current read input values for acceleration stroke S and 
vehicle speed V. 
The computer 22 then determines the throttle sensitivity Thg according to 
the aforementioned equation (1) using the throttle sensitivity model 
output Thx in step 1004. 
Next, the computer 22, in step 1005, sends the currently determined 
throttle sensitivity Thg and acceleration stroke S to the computer 21. 
Alternatively, the computer 22 multiplies the throttle sensitivity Thg by 
the acceleration stroke S to obtain the target throttle angle Thg.S, and 
sends that value to the computer 21. 
At the following step 1006, the computer 22 determines whether or not the 
drive start condition is met. When this decision condition is satisfied, 
the computer 22 moves to step 1007 to set the drive start flag F1 to "1" 
and then goes to step 1008. 
When the conditional at step 1006 is not met, the computer 22 moves to step 
1013 to determine if the drive start flag F1 is "1". That is, at step 
1006, only the drive start timing is detected and not whether starting or 
accelerated cruising is in progress. If the drive start condition has been 
met and the flag F1 is "1", indicating that starting and accelerating 
cruising is in progress, the computer 22 moves from step 1006 to step 1013 
and then to step 1008. 
At step 1008, the computer 22 determines whether or not the drive start end 
condition is met. If so, the computer 22 moves to step 1014. When that 
conditional is not met, i.e., when the starting and accelerating cruising 
is in progress, the computer 22 moves to step 1009. There, the computer 22 
increases the count value .tau. by "1" and performs the same processes as 
those of steps 104 and 105 in FIG. 8. That is, first the computer 22 at 
step 1010 performs the learning of the "acceleration model" according to 
the requirements of the driver DR and using the acceleration G of the 
vehicle 1 as "teaching data" More specifically, the computer 22 treats the 
acceleration G of the vehicle 1 as "teaching data", and then learns the 
relationship among the acceleration stroke S, the vehicle speed V and the 
acceleration G as the "acceleration model" so as to reduce the deviation 
to data within the "teaching data". 
Next, the computer 22 obtains the difference (acceleration deviation 
.DELTA.G) between the acceleration G and the acceleration model output Gx. 
In step 1012, the computer 22 learns the "throttle sensitivity model" 
using that acceleration deviation .DELTA.G as an "error signal". In other 
words, using the acceleration deviation .DELTA.G as the "error signal", 
the computer 22 learns the relationship among the acceleration stroke S, 
the vehicle speed V and the throttle angle Th as the "throttle sensitivity 
model" in such a way as to reduce the error portion of the "error signal". 
Learning control using the neural network technology is carried out in this 
manner with "acceleration model" characteristics being learned in response 
to the acceleration required by the driver DR and with a corresponding 
learning of the "throttle sensitivity model". Here, the "weighting 
coefficients" of the synapses sp as the characteristics of the 
"acceleration model" and "throttle sensitivity model", which are 
occasionally learned, are rewritten and stored in the backup RAM 26. 
After executing the process of step 1012, the computer 22 temporarily 
terminates subsequent processing. When "0.1 sec" period passes after the 
current learning control routine has started, the computer 22 executes the 
sequence of processes starting with step 1001. 
When the conditional at step 1008 is met, the computer 22 moves to step 
1014 to determine whether or not the following two conditions are 
satisfied: whether count value .tau. is smaller than a first predetermined 
value T1 (a value corresponding to 3 sec, for example), and whether the 
throttle sensitivity Thg is smaller than a second predetermined value THG 
(e.g., 0.8). 
When the decision condition in step 1014 is not met, i.e., when either one 
of the above two conditions are not met, the computer 22 goes to step 1017 
where it memorizes the learning states of the acceleration and throttle 
sensitivity models at the end of the current starting period. It is here 
then that the "weighting coefficient" for the requested-acceleration model 
and throttle sensitivity model are stored. 
Subsequently, the computer 22 resets the count value .tau. and the learning 
start flag F1 to "0" and temporarily terminates subsequent processing at 
step 1018. When "0.1 sec" passes following the beginning of the current 
learning control routine, the computer 22 executes the sequence of 
processes starting with step 1001 again. 
When the conditional at step 1014 is met, the computer 22 determines that 
the vehicle has started, advanced and stopped several times causing a 
slight change to have been registered in acceleration G. Consequently, the 
throttle sensitivity Thg is adjusted to a very low value and the learning 
control routine then moves to step 1015. This behavior of the vehicle 1 
may be seen when the vehicle 1 is driving on a branch line and approaches 
an intersection with poor visibility, such as a T road, and moves and 
stops before actual starting to join the trunk line. The above situation 
may also occur when the vehicle 1 is running on a road with a traffic jam 
and repeatedly experiences short starts and stops. 
The computer 22 at step 1015 then invalidates the "weighting coefficients" 
of the current acceleration and throttle sensitivity models, reads the 
stored "weighting coefficients" provided at the end of the previous start, 
and sets these coefficients for the acceleration and throttle sensitivity 
models. In this way, the learning states of the current acceleration and 
throttle sensitivity models are set with reference to the learning states 
provided at the end of the previous starting period. 
In step 1016, the computer 22 resets the count value .tau. and the learning 
start flag F1 to "0" and temporarily terminates the subsequent processing. 
When "0.1 sec" passes after the current learning control routine has 
started, the computer 22 executes the sequence of processing starting with 
step 1001. 
Through the execution of steps 1003, 1004 and 1005 in the named order in 
the next control cycle, the throttle sensitivity Thg can be set back to 
the value attained at the end of the previous start. 
The seventh embodiment therefore has the following function and advantages 
in addition to those of the first embodiment. 
When the vehicle 1 repeatedly makes several short starts and stops, a 
change in acceleration G is small and the throttle sensitivity Thg is thus 
given a very low value. According to the seventh embodiment, the start 
sustain time (count value .tau.) from the point where the start action of 
the vehicle 1 is detected to the point where the end of the start action 
is detected is measured. When the count value .tau. is smaller than a 
first predetermined value T, and the throttle sensitivity Thg at the end 
of the start action is smaller than a second predetermined value THG, 
computer 22 invalidates the operational learning of the acceleration and 
throttle sensitivity model for the current starting action. The learning 
states of the acceleration and throttle sensitivity models are reset with 
values provided at the end of the previous start period. As a result, the 
throttle sensitivity Thg obtained at the previous start, rather than that 
obtained in the learning process for the current start period, will be 
sent to the computer 21. Thus the throttle sensitivity Thg obtained at the 
previous start is the sensitivity obtained immediately before the vehicle 
repeatedly makes short starts, advances and stops, and it is larger than 
the throttle sensitivity Thg at the end of the current start period. 
When short starts and stops are repeated as mentioned above, the throttle 
sensitivity Thg is adjusted to a very low value. Should rapid acceleration 
be needed for the actual start action after the completion of a start 
action as above described, the throttle sensitivity Thg output to the 
computer 21 will be changed to a high value. The opening/closing of the 
throttle valve 7 is controlled in such a way that the target throttle 
angle Thg.S, obtained by multiplying the switched throttle sensitivity Thg 
by the acceleration stroke S, matches the throttle angle Th. The 
manipulation of the accelerator pedal 10 required to control the output of 
the engine 2 becomes suitable for quick acceleration in response to the 
requirements of the driver DR. 
Suppose that the vehicle 1 had started moving at time t41 and stopped at 
time t42 as shown in FIG. 38. This would be the case when one or both of 
the conditions required in step 1014 remained unsatisfied and when 
learning had begun for the current start action. In this case, the 
learning states of the acceleration and the throttle sensitivity models or 
the "weighting coefficient", which is initiated by this start, are stored. 
Assume that the next starting period occurs at time t43 and ends at time 
t44. This would be the case when both of the conditions in step 1014 had 
been met under invalidated learning conditions for the acceleration 
throttle sensitivity models, and when the learning states had been reset 
using values obtained at the end of the previous starting period. In other 
words, the "weighting coefficients" stored at the end of the previous 
start are read out and are read by the acceleration and throttle 
sensitivity models. Accordingly, as the throttle sensitivity model is 
executed, the throttle sensitivity is set back to the one obtained at the 
end of the previous start (alternate long and two short dashes line 
changed to the solid line at time t44). Thus when quick acceleration 
becomes necessary at time t45, the throttle sensitivity can be changed 
quickly and greatly. 
Accordingly, when quick acceleration is desired, the control of the 
accelerator pedal and the corresponding driving power of the vehicle can 
be improved to more accurately reflect the intention of the driver DR. 
More specifically, when driver DR requests a rapid acceleration following 
a succession of quick starts and stops, the throttle sensitivity Thg, 
adjusted to a value lower than the second predetermined value THG (0.8), 
will be prevented from increasing gradually with the passage of time as 
indicated by the alternate long and two short dashes line in FIG. 38. The 
vehicle speed V can thus be significantly changed with only a slight 
change in acceleration stroke S. 
According to the seventh embodiment, as described above, even if the 
throttle sensitivity Thg has become low due to the repetitive short starts 
and stops of the vehicle, sufficient acceleration will be provided when 
the driver DR requests quick acceleration. 
Eighth Embodiment 
The eighth embodiment according to the present invention will now be 
described referring to FIGS. 39 through 43. 
According to the eighth embodiment, a reset switch 20 is provided to 
forcibly change the learned result of a predetermined value when the 
driver requires that the vehicle undergo rapid acceleration. 
As shown in FIG. 39, the reset switch 20 is located on the driver's side of 
the vehicle's instrument panel on the front dash board. The switch 20 can 
be a push-button type ON/OFF switch. Although switch 20 is normally set to 
the OFF state, when the driver DR requires rapid acceleration, he or she 
can physically manipulate switch 20 to the ON state. 
Such a condition could arise when, for example, the driver DR desires to 
merge his or her vehicle 1 from a subsidiary or acceleration lane into a 
main lane, or when the driver DR emerges from congested traffic to 
relatively unimpeded traffic. Under both of these conditions, the 
accelerator pedal 10 would be rapidly depressed in order to achieve the 
desired continuous and rapid acceleration. As shown in FIG. 40, an 
input/output interface circuit 27 of a neuron computer 22 connects the 
reset switch 20 to each one of the sensors 11 through 13 in the first 
embodiment. Signals from the sensors 11 through 13 and the switch 20 are 
input to a CPU 23 through the input/output interface circuit 27. 
A learning control routine, carried out by the computer 22, will now be 
described referring to FIG. 41. 
Operations of this routine are initiated when an ignition key is 
manipulated to the ON state in order to start the engine. Each operation 
of the learning control routine is carried out according to a flag F3 
whose logical state varies as indicated by the timing chart in FIG. 42. 
The flag F3 is linked to the state of the reset switch 20 and is set to 
"1" when both the engine is activated and the driver DR manipulates the 
switch 20 to the ON state. Otherwise, the state of the switch 20 is 
switched to "0", which is an OFF state. 
In the learning control routine, a throttle sensitivity (Thg) is computed 
based on the throttle sensitivity model output (Thx), but is not output to 
the throttle computer 21. Alternatively, the neuro computer 22 selects one 
of two predetermined values: a minimum value (THGmin) and a standard value 
(THGst) for input to throttle computer 21. The selected value is 
thereafter regarded as a final throttle sensitivity value. 
According to the eighth embodiment, the minimum value THGmin is set to 
"0.5" and the standard value THGst is set to "1.0". Furthermore, a 
threshold value THGa (i.e., constant value) is set in order to select 
either the minimum value THGmin or the standard value THGst in 
corresponding to the current level of the throttle sensitivity Thg. 
When the level of the throttle sensitivity Thg is smaller than the 
threshold value THGa, the minimum value THGmin is set as the final 
throttle sensitivity Thga. When the level of the throttle sensitivity is 
larger than the threshold value THGa, the standard value THGst is set as 
the final throttle sensitivity Thga. 
FIG. 42 illustrates that when the engine 2 is activated at timing t51, the 
level of the throttle sensitivity becomes larger than the threshold value 
THGa between timing instances t51 and t52, while the level of the throttle 
sensitivity Thg becomes less than the threshold value THGa between timing 
instances t52 and t53. Finally, the driver DR resets switch 20 at timing 
t53. 
Under these conditions, when the engine 2 is activated at timing interval 
t51, the computer 22 initially sets the level of the flag F3 to "1" at 
step 1101 as shown in the learning control routine of FIG. 41. At step 
1102, the computer 22 reads the vehicular speed V, acceleration stroke S 
and acceleration G based on the signals from the vehicle speed sensor 13, 
accelerator pedal sensor 11, and acceleration sensor 12 respectively. 
At step 1103, the computer 22 determines whether or not the level of the 
flag F3 is set to "1", which is the level of the flag when the engine is 
started. If Flag F3 is set to "1", the computer 22 then advances to step 
1104 where the computer 22 sets initial weight coefficients for the 
acceleration and throttle sensitivity models. The initial coefficient for 
the acceleration model is selected by averaging sample coefficient values 
generated from a survey of a large number of test drivers. On the other 
hand, the weighting coefficients for the sensitivity model are initialized 
to zero. 
At step 1105, neuro computer 22 sets the standard value THGst (i.e.,=1.0) 
as final throttle sensitivity value Thga. Next, at step 1106, the computer 
22 resets the value of the flag F3 from "1" to "0" and outputs this value 
to switch 20 in order to turn the switch off. 
At step 1107, the computer 22 determines whether or not the state of the 
switch 20 is in the ON or OFF state. When switch 20 has been switched OFF 
at step 1106, the neuro computer 22 determines that the conditional at 
step 1107 has not been met and then advances to step 1108. There, at step 
1108, the computer 22 outputs the combination of the final throttle 
sensitivity Thga set at step 1105 as well as the accelerator stroke S read 
at step 1102. Alternatively, the neuro computer 22 multiplies the final 
throttle sensitivity Thga with the accelerator stroke S, the result of 
which is input as a target throttle opening angle Thga.S to the throttle 
computer 21. 
At step 1109, the computer 22 determines whether or not the ignition key 
has been manipulated to the OFF state in order to stop the engine. In such 
a case, the conditional of step 1109 is deemed not to have been met, at 
which point the routine returns to step 1102. 
According to the above described control cycle, the level of the flag F3 is 
reset to "0" at step 1106. Consequently, when step 1103 is subsequently 
executed, the conditional at step 1103 will not be met and the computer 22 
will carry out the learning control routine starting from step 1110. The 
operations in steps 1110 and 1111 are substantially equal to those in 
steps 104 and 105 of the first embodiment. 
At step 1110, the computer 22 carries out the learning of acceleration 
model as required by the driver DR with the acceleration G of the vehicle 
1 being set as master data. That is, the computer 22 learns or correlates 
the data as between the accelerator stroke S and vehicle speed V in such a 
way to reduce the deviation between the acceleration G detected by the 
sensor 12 and the master data. 
At step 1111, the computer 22 computes the difference between the 
acceleration G and the acceleration model output Gx as acceleration 
deviation .DELTA.G. The computer 22 carries out the learning routine of 
throttle sensitivity model where the acceleration deviation .DELTA.G is 
set as an error signal. The weight coefficients of synapses sp, as the 
characteristics of acceleration and throttle sensitivity model, are 
re-written and stored in the backup RAM 26. 
At step 1112, the computer 22 computes the level of the throttle 
sensitivity Thg through the above-described equation (1) based upon the 
throttle sensitivity mode output Thx. 
At step 1113, the computer 22 determines whether or not the level of the 
throttle sensitivity Thg is less than the threshold value THGa. During the 
period between timing intervals t51 and t52 in FIG. 42, the level of the 
throttle sensitivity Thg is larger than the threshold value THGa. 
Therefore, the computer 22 determines that the conditional of step 1113 is 
unmet. At step 1114, the computer 22 sets the standard value THGst as the 
final throttle sensitivity value Thga and carries out the operations 
following step 1107. 
If the neuro computer 22 determines that the throttle sensitivity Thg at 
time interval t52 in FIG. 42 is less than the threshold value THGa, the 
conditional at step 1113 will have been met and the routine advances to 
step 1115 where the computer 22 sets the minimum value THGmin as the final 
throttle sensitivity Thga (i.e.,=0.5) and begins to process the operations 
at steps 1107 and following. 
The final throttle sensitivity Thga, output to the computer 21 at step 
1108, is maintained equal to the standard value THGst (i.e.,=1.0) during 
the period represented between t51 and t52, and is then switched to the 
minimum value THGmin (i.e.,=0.5) at time t52. Such a running condition 
indicates a nominal acceleration fluctuation level, e.g., when the driver 
DR repeatedly accelerates and stops within a short period of time such as 
on a congested road. 
Should the driver DR set switch 20 after such nominal acceleration, that 
is, at time interval t53 shown in FIG. 42, the computer 22 determines that 
the conditional at step 1107 is met and advances to step 1116 were it sets 
the level of the flag F3 from "0" to "1". At step 1117, the computer 22 
sets the standard value THGst as the final throttle sensitivity value 
Thga. That is, the computer 22 switches the final throttle sensitivity 
value Thga, previously maintained equal to the minimum value THGmin, to 
the standard value THGst. Then, the computer 22 carries out the operations 
of steps following step 1108. 
Furthermore, the state of the reset switch 20, which is switched ON by the 
driver DR, will be automatically switched OFF at the execution of step 
1106 in the successive control cycle. The above described operations are 
repeatedly carried out while the engine 2 is running. When the ignition 
key is manipulated to the OFF state in order to stop the engine 2, the 
computer 22 determines that the conditional at step 1109 is met and 
terminates the learning control routine. Accordingly, the final throttle 
sensitivity Thga or target throttle angle Thga.S is set. 
A throttle opening angle control routine carried out by the computer 21 
based upon the above described final throttle sensitivity Thga and the 
accelerator stroke S at that time will now be described referring to FIG. 
43. 
After a predetermined period of time elapses from the last initiation cycle 
of this routine, the computer 21, at step 1202, reads the throttle opening 
angle Th based upon the signal transmitted from the throttle sensor 9. The 
computer 21 reads either the final throttle sensitivity value Thga or the 
target throttle opening angle Thga.S together with the accelerator stroke 
S from the computer 22. Should the final throttle sensitivity Thga and 
accelerator stroke S be the values read, the computer 21 computes the 
target throttle opening angle Thga.S by multiplying the Thga with S. 
Next at step 1202, the computer 21 determines whether or not the current 
throttle opening angle Th is less than the target throttle opening angle 
Thga.S; if so, the computer 21 drives the DC motor 8 and rotates the 
throttle valve 7 open. At step 1204, the computer 21 reads the throttle 
opening angle Th based upon the signal transmitted from the throttle 
sensor 9. 
At step 1205, the computer 21 determines whether or not the throttle 
opening angle Th is less than the target throttle opening angle Thga.S; if 
so, the computer 21 returns to step 1203, and repeatedly carries out the 
operations of steps 1203 through 1205 in order to further open the 
throttle valve 7. However, should opening angle Th be greater than the 
target throttle opening angle, the computer 21 at step 1205 determines 
that the throttle valve 7 does not need to be further opened and 
temporarily terminates the throttle opening angle control routine. 
Alternatively, when the conditional at step 1202 as described above is not 
met, the computer 21 advances to step 1206 where it determines whether or 
not the current throttle opening angle Th is larger than the target 
throttle opening angle Thga.S. If such a condition does not exist at step 
1206, the computer 21 determines that the throttle opening angle Th is 
equal to the target throttle opening angle Thga.S and temporarily 
terminates the routine. However should the conditional at 1206 be met, the 
computer 21, at step 1207, reverses DC motor 8 to close the throttle valve 
7. Following this, at step 1208, the computer 21 reads the throttle 
opening angle Th based upon the signal transmitted from the throttle 
sensor 9. 
At step 1209, the computer 21 again determines whether throttle opening 
angle Th is larger than the target throttle opening angle Thga.S. With 
this conditional met, the computer 21 returns to step 1207 and carries out 
the operations of steps 1207 through 1209 in order to further close the 
throttle valve 7. Should the conditional at step 1209 remain unsatisfied, 
the computer 21 determines that the throttle valve 7 does not require 
further closing and terminates the routine. 
Accordingly, the throttle opening angle control routine selectively rotates 
the DC motor 8 to maintain the throttle opening angle Th in accordance 
with the target opening angle Thga.S, thereby maintaining adjustment of 
the throttle valve 7. Regulation of air flow through the air intake 
passage 3 to engine 2 is thus achieved allowing for the effective control 
of the drive power output of the engine 2. 
Whenever full-scale rapid acceleration is required, the control of the 
drive power can be achieved by improving the acceleration control ability. 
Specifically, the final throttle sensitivity Thga, which is set to the 
minimum value THGmin (i.e.,=0.5), is prevented from gradually increasing 
with the passage of time. Any minimum change in the accelerator stroke S 
can thus effect a large change in vehicle speed V. Given a low throttle 
sensitivity Thg condition whenever vehicle 1 undergoes short accelerated 
movements, should the driver DR require rapid acceleration, the final 
throttle sensitivity Thga can be manually switched to the standard value 
THGst (i.e., maximum value). When, however, the driver DR desires a slower 
acceleration, the final throttle sensitivity Thga is adjusted lower to 
allow the driver DR a less onerous accelerator pedal manipulation. 
According to the eighth embodiment, the throttle sensitivity Thg obtained 
based upon the throttle sensitivity model output Thx is not directly 
employed as the final throttle sensitivity Thga. The throttle sensitivity 
is compared with the threshold value THGa, and the result of the 
comparison controls whether the minimum value THGmin or the standard value 
THGst is selected and employed as the final throttle sensitivity Thga. 
Therefore, even when an abnormally high value of Thg results from the error 
adjustment routine of the throttle sensitivity Thg, the standard value 
THGst is set as the final throttle sensitivity Thga. Therefore, any 
abnormally high value of final throttle sensitivity Thga is prevented from 
being output to the computer 21. 
Ninth Embodiment 
The ninth embodiment according to the present invention will now be 
described referring to FIGS. 44 and 45. 
The general structure of the driving force controller and throttle opening 
angle routine in this embodiment are substantially similar to that of the 
third embodiment (referred to FIGS. 16 and 17) and first embodiment 
(referred to FIG. 9) respectively, and thus their descriptions will be 
omitted. However, the operational contents of the learning control routine 
carried out by the computer 22 in the ninth embodiment differs from the 
first embodiment in that acceleration G is used as teaching data only 
during that time when the driver intends to accelerate the vehicle 1. 
The operational contents of the learning control routine carried out by the 
computer 22 will now be described in detail referring to FIG. 44. 
Each operation of the learning control routine is carried out based on a 
program counter, which counts the number of computations of the throttle 
sensitivity Thg between a time when the vehicle 1 starts accelerating and 
when it stops accelerating. A count value of .tau. results from the 
counting function and corresponds to the beginning and ending of the 
acceleration. 
Acceleration is typically required when the vehicle 1 either takes off from 
a stopped condition, when the vehicle 1 passes other vehicles, or when the 
vehicle 1 follows an accelerating the vehicle at a constant distance. 
Conditions for initiating acceleration takeoff are satisfactorily fulfilled 
if an initial acceleration takeoff level flag F4 is set to "0", if an 
initial urging pedal acceleration flag F5 is set to "0", and if the 
braking pedal 15 is not urged. Specifically, the conditions for initiating 
acceleration takeoff are when the stop lamp switch 14 is at a OFF state, 
when deviation .DELTA.S of the accelerator stroke S (per unit time) is 
larger than a predetermined value (e.g., 3 percent (%)), and when the 
vehicle speed V is less than a predetermined value (e.g., 5 km/hr). 
The initial acceleration level takeoff flag F4 indicates whether or not the 
conditions for acceleration takeoff are fulfilled. When the conditions are 
fulfilled, the level of acceleration takeoff flag F4 is set to "1"; 
otherwise flag F4 is set to "0". 
The conditions for the initial urging pedal acceleration are met when the 
level of the stop lamp switch 14 is in the OFF state, when the deviation 
.DELTA.S of the accelerator stroke S per unit time .DELTA.T (e.g., 0.5 
seconds) is larger than the predetermined value (e.g., 5 percent (%)), and 
when the vehicle speed V is less than a predetermined value (e.g., 5 
km/hr). 
The conditions for acceleration completion will be satisfied when one of 
following conditions is met. 
(1) The conditions for the initial acceleration takeoff are satisfied and 
either the level of the initial acceleration takeoff flag F4 is equal to 
"1", or the conditions for the initial urging pedal acceleration 
initiation are met, flag F5 is equal to "1", and the stop lamp switch 14 
is set to ON from the previous manipulation of the braking pedal 15. 
(2) When a predetermined period of time elapses (e.g., 1 second) after the 
initial acceleration takeoff conditions are met, when the deviation value 
.DELTA.S of the accelerator stroke S is less than a predetermined value 
(e.g., 10 percent (%)), and when the vehicle speed V is less than a 
predetermined value (e.g., 10 km/hr). 
(3) When the conditions are met for either the initial acceleration takeoff 
or the initial urging pedal acceleration and the counted value .tau. 
equals a previously determined value 2 (e.g., 75). 
For example, if the control cycle of the learning control routine is equal 
to 0.1 second and the first predetermined value T is equal to 75, a period 
of time (i.e., acceleration lasting time=request acceleration model and 
learning lasting time of the throttle sensitivity model) in which the 
counted value .tau. reaches the predetermined value T2 would be equal to 
7.5 seconds. Furthermore, for each counted value .tau., the levels of flag 
F4 and flag F5 are initialized to "0" when the ignition key is manipulated 
to the ON state for starting the engine 2. 
When a predetermined period of time (i.e., 0.1 second) elapses from the 
initialization of the operations of last leaning control routine, the 
computer 22 carries out the operations of steps 1251 through 1253, which 
correspond to those of steps 101 through 103 in FIG. 8, respectively. At 
step 1251, the computer 22 reads the accelerator stroke S, acceleration G 
and vehicle speed V based on the signals output from the accelerator pedal 
sensor 11, acceleration sensor 12 and vehicle speed sensor 13, 
respectively. The computer 22 computes deviation .DELTA.S according to the 
read accelerator stroke S. This deviation .DELTA.S corresponds to the 
difference in values between the current and last accelerator stroke S. 
At step 1252, the computer 22 carries out the throttle sensitivity model. 
That is, the computer 22 employs currently read accelerator stroke S and 
vehicle speed V as input values and computes the throttle sensitivity 
model output Thx with reference to the previously learned throttle 
sensitivity model characteristics. Then, the computer 22 computes the 
throttle sensitivity Thg through the above described equation (1) in 
accordance with the throttle sensitivity model output Thx. 
At step 1253, the computer 22 outputs the current throttle sensitivity Thg 
and the accelerator stroke S to the computer 21. At this point, the 
computer 22 may compute the target throttle opening angle Thg.S by 
multiplying the throttle sensitivity Thg with the accelerator stroke S and 
output the result to the computer 21. 
At step 1254, the computer 22 determines whether or not the conditions for 
the acceleration take-off initiation are satisfied; if so, the computer 22 
sets the level of the flag F4 to "1", resets the level of flag F5 to "0" 
at step 1255, and then advances to step 1256. When the conditions of step 
1254 remain unsatisfied, the computer 22 advances to step 1256 without 
carrying out the operation of step 1255. 
Importantly, at step 1254 only the time of the initial acceleration takeoff 
is detected. Determining whether or not the acceleration takeoff is still 
under way is not performed. Therefore, if the conditions for the 
acceleration takeoff are satisfied and the level of the flag F4 is equal 
to "1", the computer 22 determines that the vehicle 1 is undergoing 
acceleration takeoff and skips the operation of step 1255. 
At step 1256, the computer 22 determines whether or not the conditions for 
the initial urging pedal acceleration are met. If they are met, the 
computer 22 advances to step 1257, resets the values of counted value 
.tau. and flag F1 to "0", and sets the level of flag F5 to "1". If the 
vehicle 1 is undergoing acceleration takeoff and if the conditions for the 
initial urging pedal acceleration are satisfied, the computer 22 
initializes .tau. and begins a new counting sequence. 
On the other hand, if the determination conditions of step 1256 are not 
met, the computer 22 will advance to step 1263 and there determine if the 
level of both flags F4 and F5 are set to "0". If computer 22 finds both 
flag F4 and F5 equal to "0", it then determines that neither acceleration 
takeoff or urging pedal acceleration conditions exist and then terminates 
the learning control routine. After a particular period of time (i.e., 0.1 
second) elapses from the beginning of the current leaning control routine, 
the computer 22 once again starts processing the operations described 
starting from step 1251. 
Should both conditionals at step 1263 remain unmet, that is when the level 
of flag F1 or flag F2 is equal to "1", the computer 22 then determines 
that the vehicle 1 is either experiencing acceleration takeoff or an 
urging pedal acceleration condition and then advances to step 1258. 
At step 1258, the computer 22 determines whether or not the conditions for 
acceleration termination are satisfied. If they are, the learning control 
routine advances to step 1264. Conversely, should vehicle 1 be 
experiencing acceleration takeoff or an urging pedal acceleration 
condition, the computer 22 advances to step 1259 where the value .tau. is 
increased by "1". 
The computer 22 next carries out the operations of steps 1260 through 1262 
in a manner similar to those taken in steps 104 and 105 of FIG. 8. At step 
1260, the computer 22 carries out the learning of the particular 
acceleration model necessitated by the requirements of the driver DR. 
Here, the acceleration G of the vehicle 1 is set as master data to be 
compared with the accelerator stroke S and vehicle speed V in order to 
reduce the deviation within 
At step 1261, the computer 22 computes the deviation (i.e., acceleration 
deviation .DELTA.G) between the acceleration G and the above-described 
request acceleration model output Gx. At step 1262, the computer 22 
processes the learning of throttle sensitivity model wherein the 
acceleration deviation .DELTA.G is set as an error signal. Computer 22 
recognizes the correlation of the particular throttle opening angle Th, 
the accelerator stroke S and vehicle speed V as the throttle sensitivity 
model with acceleration deviation .DELTA.G set as the error signal. 
Following step 1262, the computer 22 temporarily suspends further 
operations until 0.1 second has elapsed from the beginning of the current 
learning control routine, at which point the learning routine begins again 
at step 1251. 
At step 1264, the computer 22 resets the value of .tau. and the levels of 
flags F4 and F5 to "0" and temporarily suspends the operational routine 
starting from step 1251 until 0.1 second has elapsed since the beginning 
of the current control routine. 
In addition, according to the ninth embodiment, the learning processes of 
acceleration and throttle sensitivity models are only carried out during 
the acceleration condition. 
The computer 22 determines whether the vehicle is currently accelerating, 
decelerating or maintaining a constant speed, and learns the acceleration 
and throttle sensitivity models only when the acceleration running is 
detected. In this way, unnecessary throttle sensitivity adjustment Thg can 
be avoided. 
For example, assume the vehicle 1 is actually running at a constant speed, 
as shown in FIG. 45, between time t61 and t62. Since the vehicle is not 
accelerating, the processing or learning of acceleration and throttle 
sensitivity models are not carried out. Assume, however, the accelerator 
pedal 10 is sufficiently depressed as reflected at time t62. Here, the 
acceleration and throttle sensitivity models are processed and learned by 
neuro computer 22. 
Despite the fact that throttle sensitivity Thg may have been determined 
prior to running the vehicle at a constant or decelerated running speed, 
throttle sensitivity Thg is never rewritten with a lower value. This 
arrangement differs from the conventional arrangement where the throttle 
sensitivity Thg is adjusted independent of the running condition of 
vehicle 1. As a result of the invention according to this embodiment, a 
comfortable driving environment may be securely achieved during 
accelerated running. 
Tenth Embodiment 
The tenth embodiment according to the present invention will now be 
described referring to FIG. 46 through 50. 
As the throttle sensitivity Thg approaches a predetermined value over time, 
a new parameter called a learning value (.delta.), based on deviation 
.DELTA.G, is set to a certain value larger or smaller than the deviation 
between G and Gx, i.e., .DELTA.G. 
As shown in FIG. 46, in a multiple layered type neural network the 
accelerator stroke S and vehicle speed V are input to each one of neurons 
n1 in an input layer. A throttle sensitivity model output Thx output from 
a neuron n3 is determined through the following manner. A learning value 
(.delta.) is obtained by multiplying the deviation .DELTA.G by a 
correction factor (Kg) or by dividing the .DELTA.G by a factor of Kg. The 
weight coefficients of all the synapses in neutrons n1, n2 and n3 are 
corrected based upon the learned value .delta.. Accordingly, the throttle 
sensitivity model output Thx is determined based upon the corrected weight 
coefficients of the synapses sp. 
That is, the learned value .delta. is set by multiplying or dividing the 
acceleration G by the correction coefficient Kg. Correlation between the 
accelerator stroke S and vehicle speed V is learned as the throttle 
sensitivity model requested by the driver DR in order to decrease the 
learned value .delta.. The output result from the multiple layered type 
neural network is set as a throttle sensitivity model output Thx as shown 
in FIG. 6. 
FIG. 47 shows the learning control routine that is carried out by the 
computer 22. After the elapse of a predetermined period of time (i.e., 0.1 
second) from the beginning of the previous learning control routine, the 
computer 22 carries out the operations of steps 1301 through 1304, which 
correspond to steps 101 through 104 in FIG. 8, respectively. 
At step 1301, the computer 22 reads accelerator stroke S, acceleration G 
and vehicle speed V in response to signals from the accelerator pedal 
sensor 11, acceleration sensor 12 and vehicle speed sensor 13, 
respectively. 
Next the computer 22 at step 1302 carries out the operations of the 
throttle sensitivity model. In other words, the computer 22 sets the 
current values for accelerator stroke S and vehicle speed V as input 
values and computes a throttle sensitivity model output Thx referring to 
characteristics learned from the throttle sensitivity model. The computer 
22 then computes a throttle sensitivity Thg through the above-described 
equation (1) according to the throttle sensitivity model output Thx. 
At step 1303, the computer 22 outputs the computed throttle sensitivity Thg 
and accelerator stroke S to the computer 21. Alternatively, the computer 
22 multiplies the throttle sensitivity Thg with the accelerator stroke S 
to compute a target throttle opening angle Thg.S and outputs the result to 
the computer 21. 
At step 1304, the computer 22 learns the characteristics of the 
acceleration model as required by the driver DR with the acceleration G of 
the vehicle 1 being set as a master data. That is, the computer 22 learns 
the correlation between the acceleration stroke S and vehicle speed V as 
the acceleration model required by the driver DR in such a manner that the 
deviation between the acceleration G, detected by the sensor 12, and the 
master data is thereby minimized. 
At step 1305, the computer 22 computes the deviation (i.e,, acceleration 
deviation .DELTA.G) between the acceleration G and the acceleration model 
output Gx. 
At step 1306, the computer 22 determines the correction coefficient Kg 
through equation (16), described below, based upon the throttle 
sensitivity Thg computed in the current routine. 
EQU Kg=1.0+.vertline.Thgstd-Thg.vertline.*k2 (16) 
Here, Thgstd is a standard value, e.g., 1.0, and k2 is a positive constant 
value. The characteristic correction coefficient Kg varies as shown in 
FIG. 48. When the throttle sensitivity Thg equals a standard value (i.e., 
1.0), the correction coefficient Kg becomes equal to 1.0, regardless of 
the value of k2. On the other hand, as the throttle sensitivity Thg 
departs from the standard value, the correction coefficient Kg becomes 
larger than the value equal to 1.0. 
At step 1307, the computer 22 computes a learned value .delta. through 
equations (17) through (20), described below, based upon the acceleration 
deviation .DELTA.G and correction coefficient Kg, which are computed in 
this cycle. 
EQU If Thg.ltoreq.Thgstd, and .DELTA.G.ltoreq..phi.:.delta.=.DELTA.G Kg(17) 
EQU If Thg.ltoreq.Thgstd, and .DELTA.G&lt;.phi.:.delta.=.DELTA.G*Kg(18) 
EQU If Thg&lt;Thgstd, and AF.gtoreq..phi.:.delta..DELTA.G*Kg (19) 
EQU If Thg&lt;Thgstd and .DELTA.G&lt;.phi.:.delta.=.DELTA.G/Kg (20) 
.phi. is an arbitrary constant (e.g., .phi.=0). 
Learned value .delta. thus takes on the following characteristics. When the 
level of throttle sensitivity Thg is larger than "1" and the level of 
.DELTA.G is larger than "0", or when the level of throttle sensitivity Thg 
is less than "1" and the level of .DELTA.G is less than "0", the learned 
value .delta. is computed by dividing the acceleration deviation .DELTA.G 
by the correction coefficient Kg. This results in a value .delta., which 
is less than the value of acceleration coefficient .DELTA.G. On the other 
hand, when the level of throttle sensitivity Thg is larger than "1" and 
the level of .DELTA.G is less than "0", or when the level of throttle 
sensitivity Thg is less than "1" and the level of .DELTA.G is larger than 
"1", the learned value .delta. is computed by multiplying the acceleration 
deviation .DELTA.G with the correction coefficient Kg. This results in a 
value .delta. that is larger than the value of acceleration deviation 
.DELTA.G. 
At step 1308, the computer 22 carries out the learning processes of the 
throttle sensitivity model based upon the learned value .delta. computed 
in the current cycle. After the learning processes were carried out, the 
computer 22 does not carry out the operations at subsequent steps. The 
computer 22 does, however, learn the correlation of throttle opening angle 
Th, accelerator stroke S and vehicle speed V as the throttle sensitivity 
model in which the learned value .delta. is computed by multiplying or 
dividing the acceleration deviation .DELTA.G by the correction coefficient 
Kg. In this way .delta. is used as a learning signal for correcting 
acceleration deviation .DELTA.G. 
For example, assume the solid straight line in FIG. 6 corresponds to an 
initial value of the throttle sensitivity model. When the driver DR 
depresses the accelerator pedal 10, thereby accelerating vehicle 1, the 
acceleration G of vehicle 1 is increased and the difference between the 
acceleration model Gx and the throttle sensitivity model is generated, as 
is the acceleration deviation .DELTA.G. Accordingly, the learned value 
.delta. is computed through the above-described equations (17) through 
(20) based upon the acceleration deviation .DELTA.G. When the learned 
value .delta. is set as the learning signal and when the learning 
processes are carried out with the intention to reduce the learned value, 
the throttle sensitivity model output Thx at that time will be a new 
throttle sensitivity model output Thx. The throttle sensitivity model will 
have changed from the initial value indicated by the solid line to that 
indicated by a broken line in FIG. 6. The correlation of throttle 
sensitivity model output Thx to the accelerator S and vehicle speed V is 
thereby learned as a continuous model rather than a partially 
discontinuous one. 
Using this technique, the learning control routine can be processed using 
neural network technology. The weight coefficients of synapses sp are used 
as the acceleration and throttle sensitivity model characteristics which 
are stored in the backup RAM 26. 
Therefore, according to the tenth embodiment, the deviation between the 
acceleration G and the request acceleration model output Gx is computed, 
and the correction coefficient Kg (i.e., without the case where the 
throttle sensitivity Thg is equal to "1") is determined. A plurality of 
learned values .delta. are computed, which correspond to the various 
conditions, based upon the acceleration deviation .DELTA.G and correction 
coefficient Kg. 
As shown in FIG. 49, when the level of throttle sensitivity Thg is larger 
than "1" and the level of acceleration deviation .DELTA.G is larger than 
"0", the learned value .delta. is computed by dividing the acceleration 
deviation .DELTA.G by the correction coefficient Kg. On the other hand, 
when the level of throttle sensitivity Thg is less than "1" and the level 
of acceleration deviation .DELTA.G is less than "0", the learned value 
.delta. is computed by dividing the acceleration deviation .DELTA.G with 
the correction coefficient Kg. When the throttle sensitivity Thg departs 
from the standard value (i.e.,=1.0), a value smaller than the acceleration 
deviation .DELTA.G is set as the learned value .delta. and there is a 
decrease in the magnitude of the throttle sensitivity. 
On the other hand, when the level of throttle sensitivity Thg is larger 
than "1.0" and the level of acceleration deviation .DELTA.G is less than 
"0", the learned value .delta. is computed by multiplying the acceleration 
deviation .DELTA.G with the correction coefficient Kg. When the level of 
throttle sensitivity Thg is less than "1" and the level of acceleration 
deviation .DELTA.G is larger than "0", the learned value .delta. is 
computed by multiplying the acceleration deviation .DELTA.G with the 
correction coefficient Kg. In other words, when the level of throttle 
sensitivity Thg approaches a standard value (i.e.,=1.0), a value larger 
than the acceleration deviation .DELTA.G is set as the learned value 
.delta. and there is an increase in the magnitude of throttle sensitivity. 
Therefore, according to the solid line shown in FIG. 50, for example, when 
the driver requests a low amount of acceleration under conditions of high 
throttle sensitivity Thg, the throttle sensitivity Thg is promptly 
decreased in order to decrease the rate with which the throttle opening 
angle Th changes. This allows the acceleration G to be promptly and 
precisely controlled in response to manipulation of the accelerator pedal 
10. 
Eleventh Embodiment 
The eleventh embodiment according to the present invention will now be 
described referring to FIGS. 51 through 56. 
In this embodiment, the response in the throttle sensitivity is increased 
using various learning control parameters including the accelerator stroke 
S to generate a the learning value (.delta.) which is set larger or 
smaller than the deviation level .DELTA.G. 
As shown in FIG. 51, according to a multiple layered neural network, 
accelerator stroke S and vehicle speed V are input to each one of neurons 
n1 in the input layer. A throttle sensitivity model output Thx, output 
from a neuron n3 in the output layer, is determined as follows. It is 
possible to use the acceleration deviation .DELTA.G, first threshold value 
dg or second threshold value -dg as the learned value .delta. and to make 
corrections to the weight coefficients of all synapses sp in the neurons 
n1, n2 and n3 based on the learned value .delta.. Learned value .delta. is 
selected from the group consisting of ".DELTA.G", "dg" and "-dg". Neuro 
computer 22 updates the output Thx from the neural net in order to 
minimize learning value .delta. and, from this, is able to provide an 
updated throttle sensitivity model having characteristics as shown in FIG. 
52. 
Operations of learning control routine carried out by the computer 22 will 
now be described referring to FIG. 54. After a predetermined period of 
time (i.e.,=0.1 second) has elapsed since the last routine was initiated, 
the computer 22 carries out the operations of steps 1401 through 1404 in a 
fashion similar to that performed in steps 101 through 104 in FIG. 8, 
respectively. 
At step 1401, the computer 22 reads accelerator stroke S, acceleration G 
and vehicle speed V based upon signals from the accelerator pedal sensor 
11, acceleration sensor 12 and vehicle speed sensor 13, respectively. 
At step 1402, the computer 22 carries out the operations of throttle 
sensitivity model by reading input values for the accelerator stroke S and 
vehicle speed V. The throttle sensitivity model output Thx is computed 
using learned throttle sensitivity model characteristics as illustrated in 
FIG. 52. The computer 22 then computes a throttle sensitivity Thg through 
the equation (21), described below, based on the throttle sensitivity 
model output Thx. 
EQU Thg=a+Thx*kl (21) 
a is a standard value that is set equal to 1.0 while k1 is a positive 
constant value for purposes of this embodiment. 
At step 1403, the computer 22 outputs the throttle sensitivity Thg and 
accelerator stroke S, determined in this control cycle, to the computer 
21. Alternatively, the computer 22 computes a target throttle opening 
angle Thg.S by multiplying the throttle sensitivity Thg with the 
accelerator stroke S for output to the computer 21. 
At step 1404, the computer 22 processes learning of the acceleration model 
according to the acceleration requirements of driver DR with acceleration 
G being set as a master data. Specifically the model is learned by 
correlating data from acceleration G, accelerator stroke S and vehicle 
speed V. In this way the deviation between data for acceleration G and the 
master data is lessened. 
At step 1405, the computer 22 computes the acceleration deviation .DELTA.G 
as between the acceleration G data and the above described acceleration 
model output data Gx. 
The computer 22 next, at step 1406, determines whether the current throttle 
sensitivity level Thg is less than the predetermined standard value 
Thgstd, whether the current accelerator stroke S is larger than the 
predetermined first value S1, and whether the currently computed 
acceleration deviation level .DELTA.G is less than the first threshold 
value dg. S1 is herein set to a positive value as is the first threshold 
value dg, herein represented as dg=0.1(g) where (g) indicates a constant 
of acceleration due to gravity. 
When the conditionals at step 1406 are met, the computer 22 advances to 
step 1407 and then sets the first threshold value dg as a learned value 
.delta.. When the conditionals at step 1406 are not satisfied, the 
computer 22 advances to step 1408 where it determines whether or not the 
current throttle sensitivity level Thg is larger than the predetermined 
standard value Thgstd, whether the level of accelerator stroke S read in 
this control cycle is less than the predetermined second value S2, and 
whether the level of acceleration deviation .DELTA.G computed in this 
control cycle is larger than the second threshold value -dg. The second 
predetermined value S2 is set to a positive value while the second 
threshold value -dg is set to a negative value, e.g., -dg=-0.1(g) where 
(g) indicates a constant of acceleration due to gravity. 
When the conditionals at step 1408 are satisfied, the computer 22 advances 
to step 1409 and then sets the second threshold value -dg as a learned 
value .delta.. Alternatively, when the conditionals at step 1408 are not 
satisfied, neuro computer 22 advances to step 1410, where it sets the 
level of acceleration deviation .DELTA.G as a learned value .delta.. 
At step 1411, the computer 22 next processes or learns the 
throttle'sensitivity model where the learned value .delta. is both 
determined from correlation of data from the throttle opening angle Th, 
the accelerator stroke S and vehicle speed V, and is also set as a 
learning signal. Following this, the learning control routine of this 
embodiment is temporarily suspended. 
For example, the straight line drawn by a solid line in FIG. 52 indicates 
an initial value of the throttle sensitivity model. When the driver DR 
depresses the accelerator pedal 10 in order to increase the speed of the 
vehicle 1, the acceleration G of the vehicle 1 naturally increases, 
deviation data between the acceleration model output Gx and the 
acceleration G is generated, and acceleration deviation .DELTA.G at the 
current time is computed. The learned value .delta. is set through one of 
the operations among steps 1407, 1409 or 1410, based upon the acceleration 
deviation .DELTA.G and the accelerator stroke S. 
When the learned value .delta. is set as a learning signal, and the 
learning processes are carried out in order to reduce the learned value 
.delta., the current throttle sensitive model output Thx is updated from 
the initial value, indicated by the solid line in FIG. 52, to the value 
indicated by the broken line in FIG. 52. The entire correlation of the 
throttle sensitivity model output Thx with respect to the accelerator 
stroke S and vehicle speed V is learned as a continuous rather than a 
discontinuous model. Weight coefficients of synopses sp, as the 
characteristics of the acceleration and throttle sensitivity models, are 
rewritten and stored in the backup RAM 26. 
In general terms, the deviation (i.e., acceleration deviation .DELTA.G) 
between the acceleration G and the above-described acceleration model 
output Gx is computed, and a plurality of learned values .delta. 
corresponding to the various conditions are set based upon the 
acceleration deviation .DELTA.G, throttle sensitivity Th and accelerator 
stroke S. 
When the level of throttle sensitivity Thg is less than the standard value 
Thgstd, the level of accelerator stroke S becomes larger than the 
predetermined value S1, the level of acceleration deviation .DELTA.G 
becomes less than the first threshold value dg, and the first threshold 
value dg is set as a learned value .delta.. Therefore, when the initial 
throttle sensitivity Thg is low and the magnitude of the depressed 
accelerator pedal 10 is large, the first threshold value dg, which is 
larger than the level of acceleration devistion .DELTA.G, is set as a 
learned value .delta.. 
Therefore, as shown by the solid line in FIG. 55, for example, when 
acceleration by the driver DR is increased from when the throttle 
sensitivity Thg is low (e.g., throttle sensitivity Thg=0.5), the throttle 
sensitivity Thg is promptly increased so as to provide a more sensitive 
throttle opening angle Th response. For purposes of illustration this 
improved response is contrasted with a less responsive sensitivity angle 
opening represented by the broken line in FIG. 55. This means that 
acceleration G can be changed largely in response to the driver's 
depression of the accelerator pedal 10. 
When the level of throttle sensitivity Thg is larger than the standard 
value Thgstd, the accelerator stroke level S is less than the second 
predetermined value S2, the level of acceleration deviation .DELTA.G is 
larger than the second threshold value -dg, and the second threshold value 
-dg is set as a learned value .delta.. Given a slightly depressed 
accelerator pedal and a high initial throttle sensitivity Thg, the second 
threshold value -dg is set as learned value .delta.. Thus, when the driver 
DR requests less acceleration during a time when the throttle sensitivity 
Thg is high (e.g., throttle sensitivity Thg=1.5) as shown in FIG. 56, the 
throttle sensitivity Thg will be promptly decreased to lessen the throttle 
opening angle Th. This differs from the comparative example indicated by 
the broken line in FIG. 56. This illustrates that according to the present 
embodiment, the acceleration G can be changed by a small magnitude in 
response to the large magnitude of urged accelerator pedal 10. 
Twelfth Embodiment 
The twelfth embodiment according to the present invention will now be 
described referring to FIGS. 57 through 60. 
According to the twelfth embodiment, when the throttle sensitivity level 
Thg exceeds its predetermined maximum or minimum limits, the learned value 
is set to "0". This forces the throttle sensitivity level Thg to either 
its upper or lower limit. 
As shown in FIG. 57, according to a multiple layered neural network, 
accelerator stroke S and vehicle speed V are input to each one of neurons 
n1 in the input layer thereof. A throttle sensitivity model output Thx, 
output from a neuron n3 in the output layer, is determined using 
acceleration deviation .DELTA.G as a learned value .delta. for correcting 
the coefficients of synapses sp of the neurons n1, n2 and n3. By 
correlating the throttle opening angle, the accelerator stroke S and 
vehicle speed V, a throttle sensitivity model is learned according to the 
requirements of the driver DR in order to minimize the learned value 
.delta.. The output from the multi layered neural network is employed as a 
throttle sensitivity model output Thx. 
With reference to FIG. 58, computer 22 processes the steps at 1501 to 1504 
after a predetermined period of time (i.e.,=0.1 second) has elapsed since 
the beginning of the last control routine. Steps 1501 through 1504 are 
similar to those of steps 101 through 104 in FIG. 8, respectively. At step 
1501, the computer 22 reads accelerator stroke S, acceleration G and 
vehicle speed V based upon signals from the accelerator pedal sensor 11, 
acceleration sensor 12 and vehicle speed sensor 13. 
At step 1502, the computer 22 initiates the throttle sensitivity model 
operation by reading values for the accelerator stroke S and the vehicle 
speed V. Throttle sensitivity model output Thx is computed by referring to 
characteristics of the learned throttle sensitivity model (referred to 
FIG. 52). The computer 22 then computes a throttle sensitivity Thg through 
the equation (21), described above, using throttle sensitivity model 
output Thx. 
At step 1503, the computer 22 outputs currently determined values for the 
throttle sensitivity Thg and accelerator stroke S to the computer 21. 
Alternatively, the computer 22 computes a target throttle opening angle 
Thg.S by multiplying the throttle sensitivity by the accelerator stroke S, 
and outputs the result to the computer 21. At step 1504, the computer 22 
carries out the learning process of the particular acceleration model 
required by the driver DR. Acceleration G is both correlated with 
accelerator stroke S and vehicle speed V to produce the acceleration model 
and is set as a master data. At step 1505, the computer 22 computes the 
acceleration deviation .DELTA.G according to the acceleration G and the 
acceleration model output Gx. Next at step 1506, the computer 22 
determines whether or not the current throttle sensitivity level Thg is 
larger than the predetermined upper limit Thgmax, here "1.5" of the 
sensitive adjustment region. Also at step 1506, the currently computed 
acceleration deviation .DELTA.G is checked to determine whether it is 
larger than the predetermined value .phi.1, which according to the present 
embodiment is set to "0". Both the upper limit value Thgmax and the lower 
limit value Thgmin (i.e., Thgnin will be described afterward) of the 
sensitivity adjustment region are stored in the backup RAM 26. 
With the conditionals at step 1506 satisfied, the computer 22 advances to 
step 1507 where it sets the learned value .delta. as a predetermined value 
.phi.1. Conversely, when the determining conditions of step 1506 are not 
met, the computer 22 advances to step 1508 and determines whether the 
currently determined throttle sensitivity level Thg is less than the 
predetermined lower limit value Thgmin of the sensitivity adjustment 
region. The acceleration deviation .DELTA.G is set less than the 
predetermined value .phi.1 (i.e.,="0") and the lower limit value Thgmin is 
set to "0.5". Put differently, at step 1508 the computer 22 determines 
whether or not the level of throttle sensitivity Thg is less than the 
lower limit value Thgmin of the sensitivity adjustment region so that 
throttle sensitivity Thg is possibly further reduced. 
When the determining conditions of step 1508 are satisfied, the computer 22 
moves to step 1507 and then sets the learned value .delta. to the 
predetermined value .delta. i.e.,"0". When the determining conditions of 
step 1507 are not satisfied, the computer 22 advances to step 1509 where 
it sets the acceleration deviation .DELTA.G as the learned value .delta.. 
At step 1510, the computer 22 out the learning processes of the throttle 
sensitivity model with learned value .delta. being used as a learning 
signal. 
For illustration of this, the straight solid line provided in FIG. 52 
indicates an initial value of the throttle sensitivity model. When the 
driver DR depresses the accelerator pedal 10 to increase the speed of the 
vehicle 1, a deviation between the request acceleration model output Gx 
and the acceleration G is generated and the current value for acceleration 
deviation .DELTA.G is computed. The learned value .delta. is set through 
either one of operations of steps 1507 or 1509, based upon the 
acceleration deviation .DELTA.G and the accelerator stroke S. 
With the learned value .delta. set as a learning signal, in order to reduce 
the learned value .delta., a new throttle sensitive model output Thx is 
generated. This is illustrated in FIG. 52 with the solid line representing 
the initial value and the broken line representing new model 
characteristics. Like previous embodiments, the correlation of the 
throttle sensitivity model output Thx with accelerator stroke S and 
vehicle speed V is learned as a continuous rather than a discontinuous 
model. 
If the learned value .delta. is equal to "0", the learning processes are 
not actually carried out. In other words, when the throttle sensitivity 
Thg is larger than the upper limit value Thgmax, and when the acceleration 
deviation .DELTA.G is larger than the predetermined value .phi., the 
learned value .delta. is set as a predetermined value i.e., "0". Similarly 
for the case when the level of throttle sensitivity is less than the lower 
limit value Thgmin and the acceleration deviation .DELTA.G is less than 
the predetermined value .phi.. Here also, the learned value .delta. is set 
as a predetermined value i.e., "0". In either case, the learning process 
is not actually out. The level of throttle sensitivity Thg is maintained 
equal to either the upper limit value Thgmax or the lower limit value 
Thgmin of the sensitivity adjustment region. Weight coefficients of 
synopses sp are stored in the backup RAM 26 as the characteristics of 
acceleration and throttle sensitivity models. 
Thus, according to the twelfth embodiment, the learning processes are never 
actually carried out in the region above the upper limit value Thgmax. 
Consequently, the level of throttle sensitivity Thg never exceeds but 
rather is maintained at the upper limit value Thgmax. 
FIG. 59 is provided to illustrate the change in the throttle sensitivity 
Thg when the acceleration of vehicle 1 is rapidly decreased at time t71. 
The solid line in FIG. 59 indicates the present embodiment while the 
broken line indicates a contrasting control configuration. In the 
comparative example, the level of throttle sensitivity Thg increases from 
time t71 to time t72, where it reaches the upper limit value Thgmax, and 
it continues to increase until time 73. According to the present 
embodiment, however, after the throttle sensitivity reaches the upper 
limit value Thgmax at time t72, it remains constant at Thgmax until the 
acceleration required by driver DR begins to decrease at time t73. This 
advantageously allows for superior response in throttle sensitivity Thg, 
especially in situations necessitating a rapid decrease in sensitivity 
level Thg, since the throttle sensitivity Thg never exceeds the upper 
limit value Thgmax. 
Likewise, the actual learning processes are never carried out in the region 
below the lower limit value Thgmin, so that the level of throttle 
sensitivity Thg never drops below the lower limit value Thgmin. In this 
case, the level of throttle sensitivity Thg is maintained at a value equal 
to the lower limit value Thgmin. 
FIG. 60 shows the change in the throttle sensitivity Thg when the request 
acceleration is rapidly increased after a period of decreasing 
acceleration. The solid line in FIG. 60 indicates the actual embodiment, 
and the broken line thereof indicates the comparative embodiment. 
According to the comparative embodiment, the throttle sensitivity Thg 
decreases from time t81, and reaches the lower limit value Thgmin at time 
t82. Thereafter, the throttle sensitivity Thg continuously decreases until 
time t83. 
However, according to the present embodiment, the throttle sensitivity Thg 
decreases only until it reaches the lower limit value Thgmin at time t82, 
where remains at value Thgmin until an increase in acceleration is 
required by the driver DR. The present embodiment allows the throttle 
sensitivity Thg to undergo rapid increases since the throttle sensitivity 
Thg never drops below the lower limit value Thgmin. This results in a 
superior vehicle power control by means of improved throttle sensitivity 
to changes in acceleration required by the driver DR. 
Thirteenth Embodiment 
The thirteenth embodiment according to the present invention will now be 
described referring to FIGS. 61 and 62. 
According to the thirteenth embodiment, a "learning rate" is employed in 
the learning control routine to improve the learning of the throttle 
sensitivity model by neuro computer 22 given a condition where a 
decreasing throttle sensitivity level Thg is larger than a predetermined 
standard value. 
FIG. 61 shows a learning control routine that is carried out by the neuro 
computer 22 according to the thirteenth embodiment. The learning program, 
which is stored in the ROM 24, uses the neural network technology and 
includes a data parameter called a "learning rate" (.epsilon.). This new 
parameter supplements the use of the "weighting coefficients" used in 
previous embodiments in order to more accurately determine the throttle 
sensitivity level. Generally, in the field of Neural Network Technology 
the term "learning rate" describes the process by which previously 
acquired data is processed with newly acquired data to control the 
progression of subsequent computer routines. The learning rates in the 
first through twelfth embodiments are effectively a constant value and are 
therefore not referred to by the individual routines. However, the 
learning rate .epsilon. in the thirteenth embodiment varies according to 
vehicle conditions and, therefore, is used by the routine. 
The learning control routine according to this embodiment is described in 
FIG. 61. The routine is initialized only after period of time (i.e., 0.1 
second) after the beginning of the previous routine. In this embodiment, 
steps 1601 through 1604 are similar to the steps 101 through 104 of the 
routine described in FIG. 8. 
At step 1601, the computer 22 reads accelerator stroke S, acceleration G 
and vehicle V, based on the accelerator pedal sensor 11, acceleration 
sensor 12 and vehicle speed sensor 13, respectively. 
At step 1602, the computer 22 caries out the throttle sensitivity model. In 
other words, the computer 22 sets the currently read accelerator stroke S 
and vehicle speed V as input values and computes a throttle sensitivity 
model output Thx referring to the learned characteristics of the throttle 
sensitivity model (referred to FIG. 6). The computer 22 then computes a 
throttle sensitivity Thg through the above described equation (21) 
according to the throttle sensitivity model output Thx. 
At step 1603, the computer 22 outputs the computed throttle sensitivity Thg 
and accelerators stroke S to the computer 21. Alternatively, the computer 
22 multiplies the throttle sensitivity Thg by the accelerator stroke S to 
compute a target throttle opening angle Thg.S and outputs the result to 
the computer 21. 
At step 1604, the computer 22 carries out the learning operation of the 
acceleration model according to the particular acceleration requirements 
of the driver DR. In the present embodiment acceleration G of the vehicle 
1 is set as a master data. That is, the computer 22 learns the correlation 
between the acceleration stroke S and vehicle speed V such that the 
deviation between the acceleration G detected by the sensor 12 and the 
master data is effectively minimized. 
At step 1605, the computer 22 computes the deviation (i.e., acceleration 
deviation .DELTA.G) between the acceleration G and the request 
acceleration model output Gx. 
At step 1606, the computer 22 determines whether or not the level of 
throttle sensitivity Thg computed in this control cycle is larger than the 
predetermined standard value Thgstd and whether the acceleration deviation 
.DELTA.G computed in this control cycle is less than the predetermined 
value .phi.1. The standard value Thgstd is, according to this embodiment, 
equal to "1.0" while the predetermined value .phi.1 is set equal to "0". 
This in effect results in the computer 22, at step 1606, determining 
whether or not the throttle sensitivity Thg is decreasing from the 
condition where it is larger than "1.0". When the acceleration level 
becomes small enough, the acceleration deviation .DELTA.G will be 
determined as a negative value. 
When the conditional at step 1606 is satisfied, the computer 22 multiplies 
the standard learning rate .epsilon..sub.o by the constant value k2 and 
sets the result of this multiplication as a learning rate .epsilon.. 
However, according to the thirteenth embodiment, the standard learning 
rate .epsilon..sub.o is equal to "0.2" and the constant value k2 is equal 
to "0.5". That is, at step 1607, the learning rate .epsilon. is set to 
EQU "0.2*0.5=0.1". 
On the other hand, when the conditional at step 1608 is not met, the 
computer 22 sets the standard learning rate .epsilon..sub.o as the 
learning rate .epsilon.. Therefore, at step 1608, the learning rate 
.epsilon. would be set to "0.2". 
At step 1609, the computer 22 carries out the operations of the throttle 
sensitivity model using the learning rate .epsilon. set at either step 
1607 or 1608 and with the acceleration deviation .DELTA.G computed in this 
control cycle as an error signal. In other words, the computer 22 learns 
the correlation of throttle opening angle Th with respect to the 
accelerator stroke S and vehicle speed V as the throttle sensitivity 
model. After this, the computer 22 temporarily discontinues the learning 
control routine. 
To illustrate the above explained routine, assume a solid straight line in 
FIG. 6 corresponds to an initial value of the throttle sensitivity model. 
When the driver DR depresses the accelerator pedal 10 in order to 
accelerate the vehicle 1, the acceleration G of vehicle 1 is increased and 
the difference between the acceleration model Gx and the throttle 
sensitivity model is generated so that the acceleration deviation .DELTA.G 
at that time is computed. With .DELTA.G set as an error signal, the 
throttle sensitivity model output Thx changes from the initial value 
indicated by the solid line to the characteristic indicated by the broken 
line in FIG. 7. That is, the entire correlation of throttle sensitivity 
model output Thx with respect to the accelerator S and vehicle speed V is 
learned as a continuous model rather than a discontinuous model. 
However, in the case where the throttle sensitivity Thg is larger than the 
standard value Thgstd and the acceleration deviation .DELTA.G is less than 
the predetermined value .phi., the learning rate .epsilon. is set to a 
small value "0.1". Otherwise, the learning rate .epsilon. would be set to 
the relatively larger value "0.2". When the throttle sensitivity Thg is 
larger than the standard value Thgstd and the acceleration deviation 
.DELTA.G is less than the predetermined value .phi., the rate with which 
the stored throttle sensitivity data Thg is used increases. 
Therefore, as shown in FIG. 62, when the throttle sensitivity Thg 
decreases, the magnitude of the changing sensitivity is small compared to 
when the throttle sensitivity rate increases. In other words, the throttle 
sensitivity Thg becomes less sensitive for a longer period of time when 
Thg decreases versus the condition when Thg increases. Therefore, the 
changing rate of sensitivity can be preferably matched to the driver's 
intentions. 
Although only thirteen embodiments of the present invention have been 
described in detail herein, it should be apparent to those skilled in the 
art that the present invention may be embodied in many other specific 
forms without departing from the spirit or scope of the invention. 
Particularly, it should be understood that the following modes may be 
applied. 
According to the first through thirteenth embodiments, the gasoline engine 
2 is employed as the power source of the vehicle 1. An electric motor such 
as a DC motor in an electric vehicle can be employed as a power source 
thereof. If the electric motor is employed, an electric current control 
circuit for controlling the currents to the electric motor should be 
employed in place of the linkless type throttle valve which is for 
controlling the output power from the engine 2. 
According to the first through thirteenth embodiments, an accelerator lever 
or other manipulating parts can be employed in place of the accelerator 
pedal 10 that is manipulated by the driver DR. 
According to the first through thirteenth embodiments, a sensor for 
detecting the magnitude of the force applied to the accelerator pedal 10 
can be employed in place of the accelerator pedal sensor 11 that is for 
detecting the accelerator stroke S. Furthermore, both an accelerator pedal 
sensor for detecting the accelerator stroke S and a sensor for detecting 
the magnitude of the force applied to the accelerator pedal can be 
employed in combination. 
According to the first through thirteenth embodiments, acceleration of the 
vehicle 1 is directly detected by means of the acceleration sensor 12. 
However, an acceleration G can be computed by taking time differential of 
vehicle speed V, detected by means of the vehicle speed sensor 13. 
According to the first through thirteenth embodiments, throttle sensitivity 
Thg is computed through either equation (1) or equation (2) while a 
throttle sensitivity model outputed Thx is set as a correction value for 
the throttle sensitivity. The throttle sensitivity Thg can be directly 
output. If this arrangement is employed, an initial value thereof when the 
vehicle 1 is just to be shipped out of the factory is learned to output a 
standard value a. 
According to the first through thirteenth embodiments, a multi layered 
neural network is employed as a neural network technology in the neuron 
computer 22. A mutual connection type neural network can also be employed. 
According to the second embodiment, divisors M and N for the accelerator 
stroke S and vehicle speed V as parameters X and Y in the maps are set to 
"4", respectively. The divisors M and N can be preferably increased or 
deceased. However, when the divisors M and N are increased, the presumed 
error through the map is decreased and the operational period of time for 
learning becomes long. 
According to the second embodiment, the function Wxy reflects the influence 
that one data section of the data map has from another section. The 
function Wxy may be replaced with a new function as the inverse of the 
squared distance (1/Dxy.sup.2). 
According to the second embodiment, the accelerator stroke S and vehicle 
speed V are equally divided as the parameters X and Y in each map. 
Parameters X and Y can be differentially divided. For example, a small 
region of accelerator stroke S can be finely divided. 
According to the third embodiment, a standard value THG.sub.o of a learning 
control routine in FIG. 15 can be a mean value located between the minimum 
value THGmin and the maximum value THGmax of throttle opening angle Thg. 
The standard value THG.sub.o can be altered within a region that satisfies 
the abovedescribed condition with a value equaling "1.0". 
According to the third embodiment, a correction coefficient mi for 
adjusting the throttle sensitivity Thg is set to correspond to the 
characteristic indicated in the map of FIG. 16. However, the 
characteristic can be changed. For example, as shown by a double-dotted 
broken line in FIG. 19, the correction coefficient mi can be set to a 
fixed value regardless of number of the adjusting operations carried out 
to adjust the throttle sensitivity. If this arrangement is employed, the 
throttle sensitivity Thg shown by the double dotted line in FIG. 20 is to 
be changed by a constant rate (in this case, decreasing). 
According to the third embodiment, the throttle sensitivity Thg gradually 
converges to the standard value THG.sub.o as time elapses. However, the 
throttle sensitivity Thg can instantly converge to the standard value 
THG.sub.o. 
According to the fourth embodiment, when the absolute value of changing 
value as of accelerator stroke S and vehicle speed V are larger than 
arbitrary determined values respectively, it is determined to start 
learning. However, the learning processes can begin based upon conditions 
beside the above-described condition. For example, when the acceleration G 
is larger than the arbitrary determined value, it can be determined to 
begin the learning processes. 
According to the fourth, fifth and sixth embodiments, when the count value 
Ti is initialized and the vehicle speed V and accelerator stroke S are 
larger than arbitrary determined values respectively, it can be determined 
that the learning processes are being carried out. For example, when a 
brake sensor for detecting the manipulation of the brake pedal is provided 
to the brake pedal, which is manipulated for controlling the vehicle 1, 
and when the brake sensor does not detect the manipulation of the brake 
pedal after the learning processes were carried out, it can be determined 
that learning processes are being carried out. 
According to the fourth embodiment, when absolute values of changing values 
.DELTA.S, .DELTA.V and .DELTA.Gt are less than the arbitrary determined 
values respectively, it can be determined that the vehicle is in a 
constant speed driving condition. However, when any one of absolute values 
of changing magnitudes .DELTA.S, .DELTA.V and .DELTA.Gt is less than the 
arbitrary set value, it can be determined that the constant speed driving 
condition is being learned. 
According to the fifth and sixth embodiments, when the absolute value of 
change value .DELTA.S and vehicle speed V are larger than the arbitrary 
values respectively, it can be determined that the learning processes are 
underway. However, the determination for initiation of the learning 
processes can be determined, based upon when the acceleration g is larger 
than a predetermined value. 
According to the fifth embodiment, a steering angle OS, larger than the 
predetermined value a, can last over the predetermined period of time 
.beta. during that period when the vehicle 1 is determined to be turning. 
However, it can be determined immediately that the vehicle is turning. 
According to fifth embodiment, the steered angle OS detected by means of 
the steering angle sensor is employed for detecting the turning condition 
of vehicle 1. However, the turning condition of vehicle 1 can be detected 
by means of Yaw rate sensor which is disposed in the vehicle 1. 
Furthermore, the turning condition of vehicle 1 can be detected based on 
the left and right directional accelerations detected by means of left and 
right acceleration sensors disposed in the vehicle. Furthermore, the 
steering angle OS detected by means of the steering angle sensor 8 and the 
Yaw rate detected by means of Yaw rate sensor can be combined to detect 
the turning condition. This combination can, with a high degree of 
accuracy, detect the vehicle's turning condition. 
According to the sixth embodiment, a shift position SP, detected by means 
of the shift position sensor 15, is employed for detecting the reverse 
condition of the vehicle 1. However, as direction of forward or reverse 
rotation of front wheels can be detected by means of the vehicle speed 
sensor, the reverse driving condition of the vehicle can be determined 
based upon the detected rotational direction. 
According to the sixth embodiment, the present invention is embodied in a 
vehicle having an automatic transmission. However, it can be embodied in a 
vehicle having a manual transmission. If this arrangement is employed, the 
shift position sensor detects the shift position of the manual 
transmission similar to the automatic transmission. When the shift 
position is in the reverse position, the throttle sensitivity preferable 
for reverse direction is employed. When the shift position is in the above 
first gear position, the learning processes of throttle sensitivity Thg 
will be carried out. 
According to eighth embodiment, the push button type switch is employed as 
the reset switch 14. However, a lever or knob which is able to be 
connected or disconnected by rotating thereof, or various operative 
switches such as a toggle can be employed as the reset switch 14. 
According to the eighth embodiment, when the engine 1 is to be activated or 
when the switch 14 is not manipulated, either one of the standard value 
THGst or the minimum value THGmin is set as the final throttle sensitivity 
Thga. The throttle sensitivity Thg computed in accordance with the 
throttle sensitivity model output Thx at that time can be directly set as 
the final throttle sensitivity Thga. 
According to the ninth embodiment, the conditions for detecting the 
take-off and urging pedal acceleration respectively, the changing value 
.DELTA.S should be set larger than the predetermined value. It can be 
altered in the following manner. The conditional can be satisfied when the 
changing value of acceleration G exceeds the predetermined value, or when 
the magnitude of urging force applied on the accelerator pedal exceeds the 
predetermined value in place of the accelerator stroke S in the above 
described condition (3). Furthermore, the above-described condition can be 
set to be satisfied when the changing value of urging force applied on the 
accelerator pedal exceeds the predetermined value. 
According to the tenth embodiment, when the throttle sensitivity Thg 
approaches the standard value (i.e.,=1.0), the learned value .delta. can 
be computed by multiplying the acceleration deviation .DELTA.G with the 
correction coefficient Kg. However, the learned value .delta. can be 
computed by adding the acceleration coefficient .DELTA.G to the correction 
coefficient Kg. 
Furthermore, according to the tenth embodiment, when the throttle 
sensitivity Thg departs from the standard value (i.e.,=1.0), the value 
.delta. is computed by dividing the acceleration deviation .DELTA.G by the 
correction coefficient Kg. However, the learned value .delta. can be 
computed by subtracting the correction coefficient Kg from the 
acceleration deviation .DELTA.G, or can be directly set the acceleration 
deviation .DELTA.G as the learned value .delta.. 
According to the tenth embodiment, the correction coefficient Kg is 
computed through the equation (16) based upon the throttle sensitivity 
Thg. However, the correction coefficient Kg can be computed through other 
equations or it can be set as a predetermined value. 
According to the eleventh embodiment, given the conditions when the 
throttle sensitivity Thg is less than the standard value Thgstd, the 
accelerator stroke S is larger than the predetermined value S1, and the 
acceleration deviation .DELTA.G is less than the first threshold value dg, 
then the first threshold value dg is set as the learned value .delta.. 
However, the learned value .delta. can be set to any value larger than the 
acceleration deviation .DELTA.G. 
Furthermore, according to the eleventh embodiment, under the conditions 
when the throttle sensitivity Thg is larger than the standard value 
Thgstd, the accelerator stroke S is less than the second predetermined 
value S2, and the accelerator deviation .DELTA.G is larger (i.e., higher) 
than the second threshold value -dg, then the second threshold value -dg 
is set as the learned value .delta.. However, the throttle sensitivity Thg 
can be set to any negative value less than the acceleration deviation 
.DELTA.G. 
Furthermore, according to the eleventh embodiment, the acceleration 
deviation .DELTA.G is directly set as the learned value .delta.. However, 
if the above-described condition is applied, the learned sensitivity value 
.delta. can be set to any value excluding the acceleration deviation 
.DELTA.G. 
According to the twelfth embodiment, when the throttle sensitivity Thg is 
larger than the upper limit value Thgmax and the acceleration deviation 
.DELTA.G is larger than the predetermined value .phi., or when the 
throttle sensitivity Thg is less than the lower limit value Thgmin and the 
acceleration deviation .DELTA.G is less than the predetermined value 
.phi., the learned value .delta. is set to the predetermined value .delta. 
(i.e,=0). However, the learned value .delta. can be set in the following 
manners. When the throttle sensitivity Thg is larger than the upper limit 
value Thgmax and the acceleration deviation .DELTA.G is larger than the 
predetermined value .phi. the learned value .delta. can be set to the 
predetermined negative value and the throttle sensitivity Thg can be 
designed to gradually decrease. Given this condition, the throttle 
sensitivity Thg never exceeds the upper limit value Thgmax. 
On the other hand, when the throttle sensitivity Thg is less than the lower 
limit value Thgmin and the acceleration deviation .DELTA.G than the 
predetermined value .phi., the learned value .delta. can be set to the 
predetermined positive value and the throttle sensitivity Thg can be 
designed to gradually increase. When such a case is employed, the throttle 
sensitivity Thg never goes below the lower limit value Thgmin. 
According to the thirteenth embodiment, when the throttle sensitivity Thg 
is larger than the standard value Thgstd and the acceleration deviation 
.DELTA.G is less than the predetermined value .phi., the learning rate 
.epsilon. is set to "0.1". The learning rate .epsilon. is set to "0.2' in 
other conditions. However, the learning rate .epsilon. is not limited to 
any values described above. That is, the learning rate .epsilon. should, 
when under conditions where the throttle sensitivity Thg is larger than 
the standard value Thgstd and where the acceleration deviation .DELTA.G 
the predetermined value .phi., be less than under other conditions. 
Therefore, the present examples and embodiments are to be considered as 
illustrative and not restrictive and the invention is not to be limited to 
the details given herein, but may be modified within the scope of the 
appended claims.