Patent Publication Number: US-11655791-B2

Title: Internal combustion engine control device

Description:
TECHNICAL FIELD 
     The present disclosure relates to an internal combustion engine control device. 
     BACKGROUND ART 
     Conventionally, an invention related to ignition timing control of an internal combustion engine including a variable compression ratio mechanism that varies a compression ratio is known (see PTL 1 below). This conventional internal combustion engine control device enables easy and highly accurate control of ignition timing according to a variably controlled compression ratio (see Abstract and the like of PTL 1). 
     This conventional control device sets a basic compression ratio according to an engine operating state and detects an actual compression ratio. Then, the conventional control device sequentially sets a retard correction coefficient and a retard correction amount when ΔCR&gt;0 according to the sign (positive or negative) of a compression ratio deviation ΔCR ((actual compression ratio)−(basic compression ratio)) and retards the basic ignition timing set according to an engine operating state. In addition, when ΔCR&lt;0, the conventional control device sequentially sets an advance correction coefficient and an advance correction amount to advance the ignition timing (see Abstract and the like of PTL 1). 
     CITATION LIST 
     Patent Literature 
     
         
         PTL 1: JP 2005-069130 A 
       
    
     SUMMARY OF INVENTION 
     Technical Problem 
     The conventional internal combustion engine control device controls the ignition timing based only on the compression ratio. In such control, depending on the operating condition of the internal combustion engine, a control error of the ignition timing may occur due to the influence of parameters other than the compression ratio on the ignition timing. In a case where the control error of the ignition timing is an advance side error, knocking that is improper combustion may occur in the internal combustion engine. In a case where the control error of the ignition timing is a retard side error, deterioration of thermal efficiency or combustion fluctuation may occur in the internal combustion engine. 
     The present disclosure provides an internal combustion engine control device capable of reducing a control error of the ignition timing as compared with the conventional technique. 
     Solution to Problem 
     An aspect of the present disclosure is an internal combustion engine control device including: a neural network model that receives three or more variables including at least a rotation speed, a load, and another specific variable of an internal combustion engine as inputs and outputs a control amount of the internal combustion engine, wherein the neural network model includes a first neural network model having a reference value of the specific variable as an input and a second neural network model having a current value of the specific variable as an input, and a reference value of the control amount calculated based on the rotation speed and the load is corrected using a difference or a ratio between the output of the first neural network model and the output of the second neural network model as a correction amount. 
     Advantageous Effects of Invention 
     According to the present disclosure, it is possible to provide an internal combustion engine control device capable of reducing a control error of the ignition timing as compared with the conventional technique. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG.  1    is a schematic diagram illustrating a first embodiment of an internal combustion engine control device according to the present disclosure. 
         FIG.  2    is a functional block diagram of the internal combustion engine control device illustrated in  FIG.  1   . 
         FIG.  3 A  is an explanatory diagram of a neural network model illustrated in  FIG.  2   . 
         FIG.  3 B  is an explanatory diagram of the neural network model illustrated in  FIG.  2   . 
         FIG.  3 C  is an explanatory diagram of the neural network model illustrated in  FIG.  2   . 
         FIG.  4    is an explanatory diagram of an example of a reference map illustrated in  FIG.  2   . 
         FIG.  5 A  is an explanatory diagram of a determination neural network model illustrated in  FIG.  2   . 
         FIG.  5 B  is an explanatory diagram of the determination neural network model illustrated in  FIG.  2   . 
         FIG.  5 C  is an explanatory diagram of the determination neural network model illustrated in  FIG.  2   . 
         FIG.  6    is an explanatory diagram of a determination neural network model illustrated in  FIG.  2   . 
         FIG.  7    is a flowchart for explaining a processing flow of the internal combustion engine control device illustrated in  FIG.  1   . 
         FIG.  8    is a graph for explaining an operation region in which compression ratio control by a variable compression ratio mechanism is performed. 
         FIG.  9    is a graph for explaining an operation region where EGR is introduced. 
         FIG.  10 A  is a diagram for explaining lift patterns of an intake valve and an exhaust valve. 
         FIG.  10 B  is a diagram for explaining lift patterns of an intake valve and an exhaust valve. 
         FIG.  10 C  is a diagram for explaining lift patterns of an intake valve and an exhaust valve. 
         FIG.  11    is a graph for explaining control of a variable valve mechanism of an intake valve and an exhaust valve. 
         FIG.  12    is a diagram illustrating an example of a correction map of an internal combustion engine control device according to a comparative embodiment. 
         FIG.  13 A  is a graph showing the relationship between filling efficiency and ignition timing under reference condition. 
         FIG.  13 B  is a graph showing the relationship between the filling efficiency and the ignition timing under a first correction condition. 
         FIG.  13 C  is a graph showing the relationship between the filling efficiency and the ignition timing under a second correction condition. 
         FIG.  14    is a schematic diagram illustrating a second embodiment of an internal combustion engine control device according to the present disclosure. 
         FIG.  15 A  is an explanatory diagram of a method of acquiring teacher data of MBT. 
         FIG.  15 B  is an explanatory diagram of a method of acquiring teacher data of MBT. 
         FIG.  16 A  is an explanatory diagram of a method of acquiring teacher data of a trace knock timing. 
         FIG.  16 B  is an explanatory diagram of a method of acquiring teacher data of a trace knock timing. 
         FIG.  16 C  is an explanatory diagram of a method of acquiring teacher data of a trace knock timing. 
         FIG.  16 D  is an explanatory diagram of a method of acquiring teacher data of a trace knock timing. 
         FIG.  17    is a flowchart illustrating a processing flow of the internal combustion engine control device illustrated in  FIG.  14   . 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, an embodiment of an internal combustion engine control device according to the present disclosure will be described with reference to the drawings. 
     First Embodiment 
       FIG.  1    is a schematic diagram illustrating an embodiment of an internal combustion engine control device according to the present disclosure. An internal combustion engine control device  100  of the present embodiment is configured by, for example, an electronic control unit (ECU) that controls an engine  210  of a vehicle such as an automobile, or constitutes a part of the ECU. The ECU is, for example, a microcontroller, and includes a central processing unit (CPU) (not illustrated), a storage device such as a ROM and a flash memory, various computer programs and data stored in the storage device, a timer, and an input/output unit that communicates with peripheral devices. 
     In the example illustrated in  FIG.  1   , the internal combustion engine control device  100  according to the present embodiment controls the engine  210  mounted on a vehicle such as an automobile to generate power and an engine system  200  including the peripheral devices thereof. The engine system  200  includes, for example, the engine  210  that is an internal combustion engine, and an intake flow path and an exhaust flow path connected to the engine  210 . The intake flow path of the engine  210  is provided with an air flow sensor  201 , a turbocharger  202 , an air bypass valve  203 , an intercooler  204 , a supercharging temperature sensor  205 , a throttle valve  206 , an intake manifold  207 , a supercharging pressure sensor  208 , and a flow enhancing valve  209 . 
     In addition, the engine  210  includes, for example, an intake valve  211 , an exhaust valve  212 , opening/closing position sensors  213  and  214 , a fuel injection valve  215 , an ignition plug  216 , a knock sensor  217 , a crank angle sensor  218 , and a variable compression ratio mechanism  219 . The exhaust flow path of the engine  210  is provided, for example, with a wastegate valve  220 , an air-fuel ratio sensor  221 , an exhaust purification catalyst  222 , an exhaust gas recirculation (EGR) pipe  223 , an EGR cooler  224 , an EGR temperature sensor  225 , an EGR valve  226 , and a differential pressure sensor  227 . 
     The air flow sensor  201  includes, for example, a temperature sensor, a flow rate sensor, and a humidity sensor, measures the temperature, the flow rate, and the humidity of the air taken into the intake flow path, and outputs the measurement result to the internal combustion engine control device  100 . The turbocharger  202  includes a compressor  202   a  and a turbine  202   b , rotates the turbine  202   b  by the gas flowing through the exhaust flow path, rotates the compressor  202   a  by the rotation of the turbine  202   b , and pressure-feeds the air taken into the intake flow path to the engine  210 . 
     The air bypass valve  203  is provided, for example, in a bypass flow path bypassing the turbocharger  202  in the intake flow path, and is opened and closed by a control signal from the internal combustion engine control device  100  to prevent the pressure of the air between the compressor  202   a  and the throttle valve  206  from excessively increasing. For example, when the intake manifold  207  is rapidly closed in the supercharged state, the air bypass valve  203  is opened according to the control of the internal combustion engine control device  100 . In this way, the compressed air downstream of the compressor  202   a  flows back to the upstream of the compressor  202   a  through the bypass flow path, and the supercharging pressure decreases. 
     The intercooler  204  cools the intake air whose temperature has been increased by adiabatic compression by the compressor  202   a  to lower the temperature. The supercharging temperature sensor  205  measures the temperature (supercharging temperature) of the intake air cooled by the intercooler  204 , and outputs the measurement result to the internal combustion engine control device  100 . The throttle valve  206  is provided, for example, downstream of the supercharging temperature sensor  205  to control the amount of intake air flowing into the cylinder of the engine  210  by controlling the opening degree by the internal combustion engine control device  100 . The throttle valve  206  includes, for example, a butterfly valve capable of controlling a valve opening degree by a control signal from the internal combustion engine control device  100  independently of a depression amount of an accelerator pedal by a driver of the vehicle. 
     The intake manifold  207  is provided downstream of the throttle valve  206 , and a supercharging pressure sensor  208  is assembled thereto. The supercharging pressure sensor  208  measures the pressure (that is, the supercharging pressure) of the intake air in the intake manifold  207 , and outputs the measurement result to the internal combustion engine control device  100 . 
     The intake manifold  207  and the intercooler  204  may be integrated. In this case, the volume of the intake flow path from the compressor  202   a  to the cylinder of the engine  210  can be reduced, and the responsiveness of acceleration/deceleration of the vehicle can be improved. 
     The flow enhancing valve  209  is provided downstream of the intake manifold  207  to generate a drift in the intake air and enhance turbulence generated in the flow of the air-fuel mixture in the cylinder of the engine  210 . Each of the intake valve  211  and the exhaust valve  212  is controlled by the internal combustion engine control device  100 , and includes a variable valve mechanism for continuously changing the phase of the valve opening/closing position. The opening/closing position sensors  213  and  214  are provided in the variable valve mechanisms of the intake valve  211  and the exhaust valve  212 , respectively, to detect the phases of the opening/closing positions of the intake valve  211  and the exhaust valve  212  and output the phases to the internal combustion engine control device  100 . 
     The fuel injection valve  215  is, for example, a direct injection type valve that is provided in the cylinder of the engine  210  to directly inject fuel into the cylinder. The fuel injection valve  215  may be a port injection type valve that injects fuel into the intake port. The ignition plug  216  is provided in the cylinder of the engine  210  to ignite the combustible air-fuel mixture in the cylinder by a spark of an electrode portion exposed in a head of the cylinder. The knock sensor  217  is provided in a cylinder block of the engine  210  to detect the presence or absence of knock generated in the combustion chamber. 
     The crank angle sensor  218  is assembled to a crankshaft of the engine  210  to output a signal corresponding to a rotation angle of the crankshaft as a signal indicating a rotation speed of the crankshaft to the internal combustion engine control device  100  in each combustion cycle. The variable compression ratio mechanism  219  is provided in the crank mechanism of the engine  210 , and can improve the maximum output while maintaining the thermal efficiency in the optimum state by changing the compression ratio under the control of the internal combustion engine control device  100  according to the operating state of the engine  210 . 
     The wastegate valve  220  is, for example, an electric valve which is provided in a bypass flow path bypassing the turbocharger  202  in the exhaust flow path and whose opening degree is controlled by a control signal from the internal combustion engine control device  100 . For example, the internal combustion engine control device  100  adjusts the opening degree of the wastegate valve  220  based on the supercharging pressure measured by the supercharging pressure sensor  208 , whereby it is possible to suppress a portion of the exhaust gas from passing through the bypass flow path of the exhaust flow path to be delivered to the turbine  202   b  of the turbocharger  202 . As a result, the supercharging pressure can be held at the target pressure. 
     The air-fuel ratio sensor  221  is provided, for example, downstream of the wastegate valve  220  of the exhaust flow path to measure the oxygen concentration of the exhaust gas, that is, the air-fuel ratio, and output the measurement result to the internal combustion engine control device  100 . The exhaust purification catalyst  222  is provided, for example, downstream of the air-fuel ratio sensor  221  in the exhaust flow path to purify harmful exhaust gas components such as carbon monoxide, nitrogen compounds, and unburned hydrocarbon in the exhaust gas by a catalytic reaction. 
     The EGR pipe  223  connects a portion of the exhaust flow path on the downstream side of the exhaust purification catalyst  222  and a portion of the intake flow path on the upstream side of the compressor  202   a  of the turbocharger  202 , and recirculates a portion of the exhaust gas having passed through the exhaust purification catalyst  222  to the intake flow path on the upstream side of the compressor  202   a . The EGR cooler  224  is provided in the EGR pipe  223  to cool the exhaust gas passing through the EGR pipe  223 . The EGR temperature sensor  225  is provided, for example, between the EGR cooler  224  and the EGR valve  226  to measure the temperature of the exhaust gas flowing through the EGR pipe  223 , and output the measured temperature to the internal combustion engine control device  100 . 
     The EGR valve  226  is provided, for example, between the EGR temperature sensor  225  and the intake flow path to control the flow rate of the exhaust gas recirculated from the exhaust flow path to the intake flow path by controlling the opening degree by the internal combustion engine control device  100 . The differential pressure sensor  227  is provided in the EGR pipe  223  and installed on the upstream side and the downstream side of the EGR valve  226  to measure a differential pressure between the pressure of the exhaust gas on the upstream side of the EGR valve  226  and the pressure of the exhaust gas on the downstream side of the EGR valve  226  and output the differential pressure to the internal combustion engine control device  100 . 
     For example, as described above, the internal combustion engine control device  100  is connected to various sensors constituting the engine system  200  and an actuator that drives each unit of the engine system  200 . The internal combustion engine control device  100  controls, for example, operations of actuators such as the throttle valve  206 , the intake valve  211  and the exhaust valve  212  including the variable valve mechanism, the fuel injection valve  215 , and the EGR valve  226 . In addition, the internal combustion engine control device  100  detects the operating state of the engine  210  based on signals input from various sensors, and ignites the ignition plug  216  at the timing determined according to the operating state. 
     The control error of the ignition timing of the ignition plug  216  of the engine  210  may cause problems such as knocking, deterioration of combustion efficiency, or combustion fluctuation. The internal combustion engine control device  100  according to the present embodiment has a configuration described below to reduce a control error of the ignition timing as compared with the conventional technique and prevent problems such as knocking, deterioration of combustion efficiency, or combustion fluctuation. 
       FIG.  2    is a functional block diagram of the internal combustion engine control device  100  of the present embodiment. Although details will be described later, the internal combustion engine control device  100  of the present embodiment has the following configuration as a main feature. 
     The internal combustion engine control device  100  of the present embodiment includes, for example, a neural network model  110  that receives three or more variables including at least the rotation speed RS, the load L, and another specific variable V of the engine  210 , which is an internal combustion engine, as inputs and outputs a control amount CV of the engine  210 . The neural network model  110  includes a first neural network model  111  having a reference value Vr of a specific variable V as an input and a second neural network model  112  having a current value Vp of the specific variable V as an input. The internal combustion engine control device  100  corrects a reference value CVr of the control amount CV calculated based on the rotation speed RS and the load L using a difference ΔOUT or a ratio R_OUT between the output OUT 1  of the first neural network model  111  and the output OUT 2  of the second neural network model  112  as a correction amount. 
     In the present embodiment, the control amount CV of the engine  210  which is an internal combustion engine is, for example, an optimum ignition timing. The optimum ignition timing is, for example, the minimum advance for best torque (MBT) or the trace knock timing, which is the critical ignition timing at which knocking occurs. 
     In the present embodiment, the specific variable V of the engine  210  which is an internal combustion engine is, for example, the operation amount of the variable compression ratio mechanism  219 , the operation amount of the variable valve mechanisms of the intake valve  211  and the exhaust valve  212 , the cooling water temperature of the engine  210 , the exhaust gas recirculation rate by the EGR pipe  223 , the operation amount of the flow enhancing valve  209 , the octane number of the fuel of the engine  210 , the intake air temperature, the intake air humidity, the fuel injection timing, the fuel injection rate, or the air-fuel ratio. 
     Hereinafter, the configuration of the internal combustion engine control device  100  according to the present embodiment will be described in more detail. In the example illustrated in  FIG.  2   , the internal combustion engine control device  100  includes, for example, a reference map  120  and a determination neural network model  130  in addition to the above-described neural network model  110 . The reference map  120  is configured to output the reference value CVr of the control amount CV of the engine  210  using the rotation speed RS and the load L of the engine  210  as inputs. The determination neural network model  130  is configured to receive the rotation speed RS of the engine  210 , the load L, and the current value Vp of the specific variable V as inputs, and output a determination result DR as to whether they are within or outside an adaptation region. 
       FIGS.  3 A to  3 C  are explanatory diagrams of an example of the neural network model  110 . The neural network model  110  is a mathematical model imitating a mechanism of a human cranial nerve circuit, and a weight and a bias are set to each neuron (unit) constituting the model. In addition, a function called an activation function is defined for neurons. An example thereof is shown in the following Formulas (1) and (2). Note that, in Formula (1), w is a weight, and b is a bias.
 
 z=w 1 ·a 1 +w 2 ·a 2 + . . . +Wn·an+b   (1)
 
 a=f ( z )  (2)
 
     As illustrated in  FIGS.  3 B and  3 C , the neural network model  110  includes layers of an input layer Li, an intermediate layer Lm, and an output layer Lo. Each of these layers includes a plurality of neurons. By increasing the number of neurons and the number of intermediate layers Lm, a more complex input/output nonlinear relationship can be approximated. 
     As illustrated in the graph on the right side of  FIG.  3 A , for example, a logistic function f_log and a linear function f_lin are appropriately selected and set as the activation function y=f(x). In the neural network model  110 , for example, the logistic function f_log is set as the activation function y=f(x) of the intermediate layer Lm, and the linear function f_lin is set as the activation function y=f(x) of the output layer Lo. There is a trade-off relationship between the approximation accuracy and the model scale, and the approximation accuracy and the model scale are set so as to satisfy both the requirements of the approximation accuracy and the model scale. 
     As in the supervised learning illustrated in  FIG.  3 B , for example, the operating state of the engine  210  such as the rotation speed RS, the load L, and a specific variable V of the engine  210  is set in the input layer Li. Further, for example, the control amount CV including the optimum ignition timing such as the MBT and the trace knock timing is set in the output layer Lo. Then, the input/output relationship of the operating state of the engine  210  can be approximated by performing machine learning on the weight w and the bias b of each neuron. An error backpropagation method can be applied to an algorithm of machine learning. 
     As described above, in the internal combustion engine control device  100  of the present embodiment, the neural network model  110  is a multilayer neural network model including the input layer Li, the intermediate layer Lm, and the output layer Lo. At least the rotation speed RS, the load L, and the specific variable V are set in each unit of the input layer Li, the weight w, the bias b, and the activation function y=f(x) are set in each unit of the intermediate layer Lm, and the control amount CV is set in the unit of the output layer Lo. 
     In this way, as illustrated in  FIGS.  2  and  3 C , the learned neural network model  110  can perform calculation in which the rotation speed RS, the load L, and another specific variable V are used as inputs and the control amount CV such as the optimum ignition timing is set as the outputs OUT 1  and OUT 2 . 
       FIG.  4    is an explanatory diagram of an example of the reference map  120  illustrated in  FIG.  2   . As described above, the reference map  120  is configured to output the reference value CVr of the control amount CV of the engine  210  using, for example, the rotation speed RS and the load L of the engine  210  as inputs. Here, in the internal combustion engine control device  100  of the present embodiment, for example, the filling efficiency is defined as the load L of the engine  210 . The filling efficiency is a ratio of a mass of air sucked into the cylinder in one cycle to a mass of air in a standard state corresponding to a volume of the cylinder of the engine  210 . 
     In the example illustrated in  FIG.  4   , the reference map  120  is a table in which the reference value CVr of the control amount CV of the engine  210  is defined according to the rotation speed RS of the engine  210  and the filling efficiency which is the load L of the engine  210 . In other words, the reference map  120  is a two-dimensional map in which the horizontal axis is the rotation speed RS of the engine  210 , the vertical axis is the filling efficiency which is the load L of the engine  210 , and the ignition timing control amount as the reference value CVr of the control amount CV of the engine  210  is defined. 
     With such a configuration, the reference map  120  is configured to output the reference value CVr of the control amount CV of the engine  210  using the rotation speed RS of the engine  210  and the filling efficiency which is the load L of the engine  210  as inputs. Here, the reference value CVr is, for example, an ignition timing control amount under the reference condition. The reference condition are various device states and standard atmospheric conditions defined by the rotation speed RS and the load L of the engine  210 . 
       FIGS.  5 A to  5 C  are explanatory diagrams of an example of the determination neural network model  130  illustrated in  FIG.  2   .  FIG.  5 A  is a graph illustrating an example of a method of determining an interpolation region IA, which is a learning region by the neural network model  110 , and an extrapolation region EA, which is a non-learning region, with a horizontal axis as an input parameter A and a vertical axis as an input parameter B. The input parameters A and B are, for example, any two parameters of the rotation speed RS, the load L, and the specific variable V input to the determination neural network model  130  in  FIG.  2   . 
     In general, the neural network model  110  is a regression model, and the prediction capability of the interpolation region IA of the teacher data is high while the prediction capability of the extrapolation region EA is low. Therefore, in order to reduce the error, it is necessary to appropriately exclude the calculation result of the extrapolation region EA. In the example illustrated in  FIG.  5 A , when the position is outside the lower limit values A_min and B_min of the input parameters A and B as in the regions a, b, and d, or when the position is outside the upper limit values of the input parameters A and B, it can be determined that the position is the extrapolation region EA. 
     However, in the example illustrated in  FIG.  5 A , the interpolation region IA and the extrapolation region EA cannot be determined only by the lower limit values A_min and B_min or the upper limit values of the input parameters A and B when the position is inside the lower limit values_min and B_min or the upper limit values of the input parameters A and B but is outside the adaptation region as in the region c. Therefore, the internal combustion engine control device  100  of the present embodiment includes the determination neural network model  130  that determines the interpolation region IA that is the adaptation region and the extrapolation region EA that is a non-adaptation region. 
     As illustrated in  FIGS.  5 B and  5 C , the logistic function f_log is set to the activation function y=f (x) of the intermediate layer Lm and the output layer Lo of the determination neural network model  130 . The determination neural network model  130  learns by setting (0.0) indicating an unlearned state in the output layer Lo in advance for all input conditions. Furthermore, learning is performed such that the operating state of the engine  210  when learning the ignition timing such as MBT and trace knock is set in the input layer Li, and (1.0) indicating that learning has been performed is set in the output layer Lo. 
     By performing such learning, it is possible to construct the determination neural network model  130  of the logistic regression type that outputs a value close to (1.0) in the vicinity of the learned operating condition and outputs a value close to (0.0) in the non-learned operating condition. For example, as illustrated in  FIG.  2   , using the determination neural network model  130 , it is possible to determine the interpolation region IA and the extrapolation region EA even when the position is inside the lower limit values A_min and B_min or the upper limit values of the input parameters A and B and outside the interpolation region IA as in the region c illustrated in  FIG.  5 A . 
       FIG.  6    is an explanatory diagram of the calculation method of the output layer Lo when the input parameters A and B of the determination neural network model  130  are located in the extrapolation region EA that is a non-learning region. The minimum value Min and the maximum value Max of each unit are defined from the frequency distribution of each unit value of the last layer of the intermediate layer Lm, that is, the layer before the output layer Lo with respect to the input parameters A and B of all learning conditions. As long as the input parameters A and B of the determination neural network model  130  exist in the interpolation region IA which is the learning region, the value of each unit in the final layer of the intermediate layer Lm is considered to exist in a range between the maximum value Max and the minimum value Min for each unit. 
     When it is determined that the input parameters A and B of the determination neural network model  130  are located in the extrapolation region EA that is the non-learning region, the value of each unit of the final layer of the intermediate layer Lm is diagnosed. When the unit value indicates a value equal to or larger than the maximum value Max, the maximum value Max is set, and when the unit value indicates a value equal to or smaller than the minimum value Min, the minimum value Min is set. In other words, when the determination neural network model  130 , which is the logistic regression type neural network model, outputs an index indicating that it is out of the range of the learning condition, the value of the unit constituting the final layer of the intermediate layer Lm of the neural network model  110  is limited to the range of the upper and lower limit values based on the maximum value Max and the minimum value Min of each unit. By performing the upper and lower limit processing in this manner, it is possible to appropriately prevent the neural network model  110  from outputting an abnormal value even when the input parameters A and b are located in the extrapolation region EA. 
     As described above, the internal combustion engine control device  100  of the present embodiment includes the determination neural network model  130  which is a logistic regression type neural network model that receives at least the rotation speed RS, the load L, and the specific variable V as inputs and outputs an index indicating whether or not the rotation speed RS, the load L, and the specific variable V are within the range of the learning condition of the neural network model  110 . In addition, a logistic function f_log is set as the activation function y=f (x) of the intermediate layer Lm and the output layer Lo of the determination neural network model  130 . 
     Furthermore, the determination neural network model  130  may have a function as a diagnosis unit that diagnoses the neural network model  110  on the basis of, for example, a comparison between the value of each unit of the intermediate layer Lm and the maximum value Max and the minimum value Min of each unit of the intermediate layer Lm. In this case, the determination neural network model  130  as the diagnosis unit may output the diagnosis result at the time of executing the arithmetic operation of the neural network model  110 . 
     Hereinafter, the operation of the internal combustion engine control device  100  according to the present embodiment will be described. 
     As illustrated in  FIG.  1   , for example, the internal combustion engine control device  100  calculates the rotation speed RS, the load L, and the specific variable V of the engine  210  using the measurement results output from various sensors constituting the engine system  200  as inputs. More specifically, the internal combustion engine control device  100  calculates, for example, the reference value Vr and the current value Vp of the specific variable V. 
     Here, the load L is, for example, the filling efficiency of the engine  210 . The specific variable V is, for example, one or more variables selected from the group consisting of the operation amount of the variable compression ratio mechanism  219  of the engine  210 , the operation amount of the variable valve mechanism, the cooling water temperature, the exhaust gas recirculation rate, the operation amount of the flow enhancing valve  209 , the octane value of the fuel, the intake air temperature, the intake air humidity, the fuel injection timing, the fuel injection rate, and the air-fuel ratio. 
     Next, as illustrated in  FIG.  2   , the internal combustion engine control device  100  calculates the reference value CVr of the control amount CV by the reference map  120  using the calculated rotation speed RS and load L as inputs. More specifically, as illustrated in  FIG.  4   , the reference map  120  outputs the reference value CVr of the control amount CV according to the input rotation speed RS and load L. 
     The internal combustion engine control device  100  uses the calculated rotation speed RS, load L, and reference value Vr of the specific variable V as inputs to calculate and output an estimated value of the target value (reference value) of the ignition timing using the first neural network model  111  (output OUT 1 ). In addition, the internal combustion engine control device  100  uses the calculated rotation speed RS, load L, and current value Vp of the specific variable V as inputs to calculate and output an estimated value of the current ignition timing using the second neural network model  112  (output OUT 2 ). 
     Furthermore, the internal combustion engine control device  100  calculates a difference ΔOUT or a ratio R_OUT between the output OUT 1  of the first neural network model  111  and the output OUT 2  of the second neural network model  112 . Then, the internal combustion engine control device  100  corrects the reference value CVr of the control amount CV, which is the ignition timing calculated by the reference map  120  based on the rotation speed RS and the load L, using the calculated difference ΔOUT or ratio R_OUT between the output OUT 1  and the output OUT 2  as the correction amount. 
     In general, in the case of using a neural network model, if an attempt is made to reduce the error of the control amount under the reference condition as much as possible with the approximation accuracy of the neural network model, there is a problem that the scale of the model becomes excessively large and the operation load increases. 
     On the other hand, the internal combustion engine control device  100  of the present embodiment calculates the reference value CVr of the control amount CV, which is the ignition timing, by the reference map  120  using the rotation speed RS and the load L as inputs. Then, the reference value CVr of the control amount CV is corrected by the difference ΔOUT or the ratio R_OUT between the output OUT 1  and the output OUT 2  of the neural network model  110  that receives the reference value Vr and the current value Vp of the specific variable V other than the rotation speed RS and the load L as inputs. 
     That is, in the internal combustion engine control device  100  of the present embodiment, under the reference condition, the correction amount by the neural network model  110  becomes 0, and the reference value CVr of the control amount CV, which is the output of the reference map  120 , is adopted. In this way, the internal combustion engine control device  100  of the present embodiment can maximize the accuracy of the control amount CV under the reference condition, and can realize both the calculation scale of the model and the accuracy of the model having the trade-off relationship. 
     In the example illustrated in  FIG.  2   , the internal combustion engine control device  100  includes the determination neural network model  130 . The determination neural network model  130  is a logistic regression type neural network model that receives at least the rotation speed RS, the load L, and the current value Vp of the specific variable V as inputs and outputs an index indicating whether or not the rotation speed RS, the load L, and the current value Vp are within the range of the learning condition of the neural network model  110 . In addition, as illustrated in  FIGS.  5 A and  5 B , for example, a logistic function f_log is set as the activation function y=f(x) of the intermediate layer Lm and the output layer Lo of the determination neural network model  130 . 
     With such a configuration, the determination neural network model  130  uses the rotation speed RS, the load L (filling efficiency), and the current value Vp of the specific variable V as inputs to determine whether they are within the region of the adaptation region (interpolation region IA) or outside the region of the adaptation region (extrapolation region EA) as illustrated in  FIG.  5 A . Hereinafter, an example of the operation of the neural network model  110  and the determination neural network model  130  will be described with reference to  FIG.  7   . 
       FIG.  7    is a flowchart illustrating a processing flow of the internal combustion engine control device  100 . In process P 1 , the internal combustion engine control device  100  acquires, for example, the rotation speed RS, the load L, and the specific variable V (the current value Vp and the reference value Vr) as input values of the neural network model  110 . Next, in process P 2 , the internal combustion engine control device  100  normalizes the values input to the neural network model  110  with the maximum value Max and the minimum value Min defined in advance at the time of learning as illustrated in  FIG.  6   . By performing this normalization, input parameters in the non-learning region (extrapolation region EA) are prevented from being input to the neural network model  110 . 
     Next, in process P 3 , the internal combustion engine control device  100  performs an extrapolation determination process of determining whether the input values are in the learning region (interpolation region IA) or the non-learning region (extrapolation region EA) by the determination neural network model  130 . In process P 3 , when the determination neural network model  130  determines that the input values are in the interpolation region IA (NO), the process proceeds to process P 4 . 
     In process P 4 , the internal combustion engine control device  100  performs calculation using the neural network model  110 , and then executes process P 8  described later. On the other hand, in process P 3 , when the determination neural network model  130  determines that the input values are in the extrapolation region EA (YES), the internal combustion engine control device  100  performs process P 5 . 
     In process P 5 , the internal combustion engine control device  100  diagnoses the value of each unit in the final layer of the intermediate layer Lm of the neural network model  110 . More specifically, for example, the internal combustion engine control device  100  determines whether the value of each unit in the final layer of the intermediate layer Lm of the neural network model  110  is greater than or equal to the prescribed maximum value Max or less than or equal to the minimum value Min. 
     Next, in process P 6 , when the internal combustion engine control device  100  diagnoses that the value of each unit is equal to or larger than the maximum value Max in the previous process P 5 , the maximum value Max is set for each unit in the final layer of the intermediate layer Lm of the neural network model  110 . In addition, in process P 6 , when the internal combustion engine control device  100  diagnoses that the value of each unit is equal to or less than the minimum value Min in the previous process P 5 , the minimum value Min is set for each unit in the final layer of the intermediate layer Lm of the neural network model  110 . 
     Next, in process P 7 , the internal combustion engine control device  100  performs calculation of the output layer Lo of the neural network model  110 , and then executes process P 8 . In process P 8 , an output OUT 1  and an output OUT 2  are output as calculation results of the neural network model  110 . 
     As described above, the internal combustion engine control device  100  of the present embodiment includes the neural network model  110  in which three or more variables including at least the rotation speed RS of the engine  210 , the load L, and another specific variable V are input and the control amount CV of the engine  210  is output. The neural network model  110  includes the first neural network model  111  having the reference value Vr of the specific variable V as an input and the second neural network model  112  having the current value Vp of the specific variable V as an input. Then, the internal combustion engine control device  100  of the present embodiment corrects the reference value CVr of the control amount CV calculated based on the rotation speed RS and the load L using the difference ΔOUT or the ratio R_OUT between the output OUT 1  of the first neural network model  111  and the output OUT 2  of the second neural network model  112  as the correction amount. In addition, in the internal combustion engine control device  100  of the present embodiment, the control amount CV of the engine  210  is, for example, the optimum ignition timing. 
     With such a configuration, the control amount CV can be accurately corrected by the neural network model  110  even when the control amount CV has a large influence of the interaction between the correction variables such as the rotation speed RS, the load L, and the specific variable V, for example, the optimum ignition timing such as the MBT or the trace knock timing. Furthermore, in the internal combustion engine control device  100  of the present embodiment, the reference value CVr of the control amount CV, which is the output of the reference map  120  based on the rotation speed RS and the load L, is adopted under the reference condition. Therefore, according to the internal combustion engine control device  100  of the present embodiment, it is possible to achieve a trade-off relationship between the calculation load and the accuracy of the control amount CV without requiring large-scale neural network model approximation. The control amount CV is not limited to the optimum ignition timing, and may be another control amount of the engine  210 . 
     In the internal combustion engine control device  100  according to the present embodiment, the specific variable V of the engine  210  is, for example, the operation amount of the variable compression ratio mechanism  219 , the operation amounts of the variable valve mechanisms of the intake valve  211  and the exhaust valve  212 , the cooling water temperature, the exhaust gas recirculation rate (EGR rate), the operation amount of the flow enhancing valve  209 , the octane number of the fuel, the intake air temperature, the intake air humidity, the fuel injection timing, the fuel injection rate, or the air-fuel ratio. 
     Hereinafter, with reference to  FIGS.  8  to  12   , the relationship between the rotation speed RS of the engine  210 , the load L, and another specific variable V will be described using the operation amount of the variable compression ratio mechanism  219 , the EGR rate, the operation amounts of the variable valve mechanisms of the intake valve  211  and the exhaust valve  212 , and the cooling water temperature as examples. 
       FIG.  8    is a graph for describing an operation region in which the compression ratio control by the variable compression ratio mechanism  219  is performed. In  FIG.  8   , the operation region of the engine  210  is defined with the rotation speed RS as the horizontal axis and the filling efficiency as the load L as the vertical axis. The internal combustion engine control device  100  controls the operation amount of the variable compression ratio mechanism  219  in an operation region where the filling efficiency is relatively low, the load is low and the rotation speed is low and operates the engine  210  in a high compression ratio region HCR having a relatively high compression ratio. 
     In this way, combustion energy in the cylinder of the engine  210  can be efficiently converted into kinetic energy, and high thermal efficiency can be realized. 
     On the other hand, in a relatively high-load operation region where the filling efficiency of the engine  210  is high, when the compression ratio is increased, improper combustion called knocking is likely to occur. When the ignition timing is retarded in order to prevent knocking, thermal efficiency is deteriorated. That is, an excessive high compression ratio rather causes deterioration of thermal efficiency. Therefore, the internal combustion engine control device  100  controls the operation amount of the variable compression ratio mechanism  219  in the relatively high-load operation region where the filling efficiency is high, and operates the engine  210  in a low compression ratio region LCR where the compression ratio is relatively low. In this way, knocking and deterioration of thermal efficiency can be suppressed, thermal efficiency and output can be realized in a compatible manner, and the compression ratio can be appropriately controlled based on the operating state of the engine  210 . Therefore, the thermal efficiency of the entire engine  210  can be improved. 
     As described above, the internal combustion engine control device  100  controls the operation amount of the variable compression ratio mechanism  219  based on the rotation speed RS of the engine  210  and the filling efficiency which is the load L, and controls the compression ratio of the engine  210  to the steady target state. However, since the variable compression ratio mechanism  219  incurs a response delay, even under an operating condition in which the rotation speed RS and the load L are the same, the variable compression ratio mechanism may exhibit different compression ratios depending on the immediately preceding state. 
     For example, under an acceleration condition for shifting the operating state from a low load condition set in a high compression ratio region HCR to a high load condition (supercharging region SCR) in a high compression ratio region HRC through a middle load condition (non-supercharging region NSCR) set in a middle compression ratio region MCR, the compression ratio is set to be on the higher compression ratio side than the steady target state due to the response delay of the variable compression ratio mechanism  219 . In this case, since knocking may occur, the ignition timing of the engine  210  needs to be appropriately corrected and controlled according to the current compression ratio based on the operation amount of the variable compression ratio mechanism  219 . 
     According to the internal combustion engine control device  100  of the present embodiment, due to the configuration illustrated in  FIG.  2    described above, the ignition timing of the engine  210  can be appropriately corrected and controlled according to the current compression ratio based on the operation amount of the variable compression ratio mechanism  219  using the operation amount of the variable compression ratio mechanism  219  as the specific variable V of the engine  210 . 
       FIG.  9    is a graph illustrating an operation region where EGR is introduced. In  FIG.  9   , similarly to  FIG.  8   , the operation region of the engine  210  is defined with the rotation speed RS as the horizontal axis and the filling efficiency as the load L as the vertical axis. The operation region of the engine  210  is roughly divided into a non-supercharging region NSCR and a supercharging region SCR. The internal combustion engine control device  100  controls the opening degree of the throttle valve  206  in the non-supercharging region NSCR, and controls the opening degree of the wastegate valve  220  by opening the throttle valve  206  in the supercharging range, thereby controlling the supercharging pressure and controlling the filling efficiency. As described above, by switching the means for adjusting the torque of the engine  210  between the non-supercharging region NSCR and the supercharging range SCR, the pump loss occurring in the engine  210  can be reduced, and the fuel-efficient operation can be realized. 
     Furthermore, the engine system  200  to be controlled by the internal combustion engine control device  100  of the present embodiment includes the EGR system including the EGR pipe  223 , the EGR cooler  224 , the EGR temperature sensor  225 , the EGR valve  226 , and the differential pressure sensor  227 . For example, the EGR system recirculates the exhaust gas, which has passed through the exhaust purification catalyst  222  and cooled by the EGR cooler  224 , to the cylinder of the engine  210  in the supercharging range SCR from a relatively high-load condition of the non-supercharging region NSCR of the engine  210 . In this way, the gas sucked into the cylinder of the engine  210  is diluted with the exhaust gas which is an inert gas, and it is possible to suppress improper combustion called knocking that is likely to occur under a high load condition. By suppressing the knocking, the ignition timing can be appropriately advanced and the fuel-efficient operation can be realized. 
     In this manner, the EGR is controlled to the steady target EGR rate by controlling the opening degree of the EGR valve  226  based on the rotation speed and the load of the engine  210  by the internal combustion engine control device  100 . On the other hand, the internal combustion engine control device  100  stops the EGR under the low temperature condition in which the condensed water is generated. As illustrated in  FIG.  1   , since the EGR system is provided at a position away from the cylinder of the engine  210 , the EGR rate in the cylinder cannot be immediately controlled to the target value even if the EGR valve  226  is controlled. 
     Therefore, even under the same condition of the rotation speed RS and the load L, different EGR rates may be shown. That is, when the EGR valve  226  is opened at the time of acceleration of shifting from a low load condition in which the EGR is stopped to a high load condition in which the EGR is introduced, the EGR rate in the cylinder may become lower than the steady target state due to a response delay of the EGR caused by the flow of air in the intake pipe. In this case, the ignition timing of the engine  210  may be over-advanced to cause knocking. Therefore, it is necessary to appropriately correct and control the ignition timing of the engine  210  according to the current EGR rate in the cylinder. 
     According to the internal combustion engine control device  100  of the present embodiment, due to the above-described configuration illustrated in  FIG.  2   , it is possible to appropriately correct and control the ignition timing of the engine  210  according to the EGR rate using the EGR rate as the specific variable V of the engine  210 . 
       FIGS.  10 A to  10 C  are diagrams for explaining lift patterns of the intake valve  211  and the exhaust valve  212  provided with a phase changing mechanism. In the default condition DEF illustrated in  FIG.  10 A , the internal combustion engine control device  100  sets the lift patterns of the intake valve  211  and the exhaust valve  212  to the timing at which the exhaust valve  212  closes and the intake valve  211  opens in the vicinity of the top dead center TDC. 
     In addition, in the overlap condition OLC illustrated in  FIG.  10 B , the internal combustion engine control device  100  advances the lift pattern of the intake valve  211  and retards the lift pattern of the exhaust valve  212  with respect to the lift pattern of the default condition DEF illustrated by a broken line. By controlling the variable valve mechanisms of the intake valve  211  and the exhaust valve  212  in this manner by the internal combustion engine control device  100 , it is possible to provide an overlap period in which both the intake valve  211  and the exhaust valve  212  are simultaneously opened. 
     In a case where the pressure of the exhaust flow path is higher than the pressure of the intake flow path, as illustrated in  FIG.  10 B , by providing an overlap period in the lift patterns of the intake valve  211  and the exhaust valve  212 , it is possible to implement internal EGR in which burned gas flows backward to the intake flow path. In addition, when the intake pressure is higher than the exhaust pressure, by providing an overlap period in the lift patterns of the intake valve  211  and the exhaust valve  212 , burned gas in the cylinder of the engine  210  can be scavenged to the exhaust flow path. 
     In the intake/exhaust slow closing condition DEL illustrated in  FIG.  10 C , the lift pattern of the intake valve  211  is retarded and the lift pattern of the exhaust valve  212  is retarded with respect to the default condition indicated by the broken line. By controlling the variable valve mechanisms of the intake valve  211  and the exhaust valve  212  in this manner by the control device  100 , the closing timing of the exhaust valve  212  can be set to be retarded from the bottom dead center BDC, and the effective compression ratio can be reduced. By reducing the effective compression ratio in this manner, the Miller cycle can be realized. 
     In addition, by retarding the exhaust valve  212  in the same manner as the intake valve  211 , occurrence of a negative overlap, which is a period until the intake valve  211  is opened after the exhaust valve  212  is closed, is prevented. By preventing the occurrence of the negative overlap, not only the pump loss of the engine  210  can be reduced, but also the valve opening timing of the exhaust valve  212  can be set in the vicinity of the bottom dead center BDC, so that the expansion ratio can be maximized. The method for controlling the lift patterns of the intake valve  211  and the exhaust valve  212  is not limited to the method using the variable valve mechanism. That is, the same effect can be obtained in a cam switching type variable valve system and a lift variable system. 
       FIG.  11    is a graph for explaining the control of the operation amount of the variable valve mechanisms of the intake valve  211  and the exhaust valve  212 . In  FIG.  11   , similarly to  FIGS.  8  and  9   , the operation region of the engine  210  is defined with the rotation speed RS as the horizontal axis and the filling efficiency as the load L as the vertical axis. 
     In the low-rotation speed and high-load operation region illustrated in  FIG.  11   , the overlap condition OLC illustrated in  FIG.  10 B  is set by advancing the intake valve  211  and retarding the exhaust valve  212  with respect to the default condition DEF illustrated in  FIG.  10 A . 
     In the engine system  200  including the turbocharger  202 , the pressure of the intake flow path is higher than the pressure of the exhaust flow path in a low-rotation speed and low-load operation region. Therefore, the remaining burned gas in the cylinder of the engine  210  can be scavenged by adjusting the lift patterns of the intake valve  211  and the exhaust valve  212  so as to overlap in this operation region. By scavenging the burned gas in the cylinder of the engine  210 , not only more fresh air can be sucked into the cylinder, but also the temperature of the gas in the cylinder can be lowered. Accordingly, knocking, which is improper combustion, can be prevented. 
     From the above effects, by setting the lift patterns of the intake valve  211  and the exhaust valve  212  to the overlap condition OLC in the low-rotation speed and low-load operation region, it is possible to greatly improve the acceleration of the engine  210  in the engine system  200  including the turbocharger  202 . 
     As illustrated in  FIG.  11   , in the low-rotation speed and low-load (low-filling efficiency) operation region, the intake valve  211  is retarded and the exhaust valve  212  is retarded with respect to the default condition DEF, and the intake/exhaust slow closing condition DEL is set. As described above, by controlling the variable valve mechanisms of the intake valve  211  and the exhaust valve  212  by the internal combustion engine control device  100 , it is possible to realize the Miller cycle by decreasing the effective compression ratio and increasing the expansion ratio, and to improve the thermal efficiency of the engine  210 . 
     As illustrated in  FIG.  11   , also in the high-rotation speed operation region, the variable valve mechanisms of the intake valve  211  and the intake valve  211  are set to the intake/exhaust slow closing condition DEL by the internal combustion engine control device  100 . In the high-rotation speed operation region, the intake air amount in the cylinder of the engine  210  can be increased as the phases of the intake valve  211  and the exhaust valve  212  are retarded due to the inertial effect of the intake gas. Therefore, in the high-rotation speed operation region, the maximum output of the engine  210  can be improved by setting the intake/exhaust slow closing condition DEL. 
     As described above, the phases of the lift patterns of the intake valve  211  and the exhaust valve  212  are controlled to the steady target phase by controlling the operation amount of the variable valve mechanism based on the rotation speed RS and the load L of the engine  210  by the internal combustion engine control device  100 . However, since the variable valve mechanisms of the intake valve  211  and the exhaust valve  212  incur a response delay, even under the condition in which the rotation speed RS and the load L are the same, the variable valve mechanisms may exhibit different phases depending on the immediately preceding state. 
     That is, the operation of the intake valve  211  is delayed in the acceleration state of shifting from the low-load operation region in which the intake valve  211  is set in the retarded state to the high-load operation region in which the intake valve  211  is retarded and set in the overlap condition OLC. Due to the delay in the operation of the intake valve  211 , the scavenging by the EGR into the cylinder of the engine  210  is not sufficiently performed up to the steady target state, the ignition timing is over-advanced, and knocking may occur. Therefore, the ignition timing needs to be appropriately corrected and controlled in accordance with the current phases of the intake valve  211  and the exhaust valve  212 . 
     According to the internal combustion engine control device  100  of the present embodiment, due to the above-described configuration illustrated in  FIG.  2   , the operation amount of the variable valve mechanism of the intake valve  211  and the exhaust valve  212  can be used as the specific variable V of the engine  210 . In this way, the ignition timing of the engine  210  can be appropriately corrected and controlled according to the current phases of the intake valve  211  and the exhaust valve  212  based on the operation amount of the variable valve mechanism of the intake valve  211  and the exhaust valve  212 . 
     The target control amounts of various devices constituting the engine system  200  are set to different values based on, for example, the temperature of the cooling water of the engine  210 . More specifically, the temperature of the cooling water rises from the level of the atmospheric temperature immediately after the start of the engine  210 , and is controlled to a constant temperature by the thermostat under the warm air condition. When the temperature of the cooling water is low, the heat loss due to the wall surface of the cylinder of the engine  210  is large, and knocking is less likely to occur as compared with the warm air condition. Therefore, the ignition timing is set to be corrected to the more advanced side under the high load condition. 
       FIG.  12    is a diagram illustrating an example of correction maps  120   a ,  120   b ,  120   c ,  120   d , . . . , and so on used in an internal combustion engine control device of a comparative embodiment different from the internal combustion engine control device  100  of the present embodiment. As described above, when the engine  210  is operated under conditions other than the reference condition, the ignition timing obtained by the reference map  120  as illustrated in  FIG.  4    cannot be used. Therefore, for example, in addition to the reference map  120  as illustrated in  FIG.  4   , the conventional internal combustion engine control device needs to use a plurality of correction maps  120   a ,  120   b ,  120   c ,  120   d , . . . , and so on in which the ignition timing that is the control amount CV is defined according to the rotation speed RS and the filling efficiency that is the load L of the engine  210  as illustrated in  FIG.  12   . 
     More specifically, in the example illustrated in  FIG.  12   , the correction map  120   a  is, for example, a correction map corresponding to the temperature of the cooling water, and the correction map  120   b  is, for example, a correction map corresponding to the compression ratio, that is, the operation amount of the variable compression ratio mechanism  219 . The correction map  120   c  is, for example, a correction map corresponding to the EGR rate, and the correction map  120   d  is, for example, a correction map corresponding to the operation amount of the variable valve mechanism of the intake valve  211  and the exhaust valve  212 . In addition, in the conventional internal combustion engine control device, in order to improve the accuracy of the control amount CV, it is necessary to consider the operation amount of the flow enhancing valve  209 , the octane number of the fuel, the intake air temperature, the intake air humidity, the fuel injection timing, the fuel injection rate, the air-fuel ratio, and the like as the correction map. However, when the number of correction maps is increased, the accuracy of the control amount CV is improved, but there is a problem that the internal combustion engine control device becomes large and complex. 
       FIGS.  13 A to  13 C  are graphs illustrating the relationship between the filling efficiency and the ignition timing under conditions where the rotation speed is the same and at least one of the compression ratio and the temperature of the cooling water is different.  FIG.  13 A  illustrates the relationship between the filling efficiency and the ignition timing under the reference condition.  FIG.  13 B  illustrates the relationship between the filling efficiency and the ignition timing under a first correction condition having a compression ratio higher than the reference condition.  FIG.  13 C  illustrates the relationship between the filling efficiency and the ignition timing under a second correction condition having a higher compression ratio and a higher temperature of the cooling water than the reference condition. 
     Under the reference condition illustrated in  FIG.  13 A , the trace knock timing TKL and the MBT are set to be on the retard side as the filling efficiency, which is the load L of the engine  210 , increases in order to prevent knocking. As illustrated in  FIG.  13 B , both the trace knock timing TKL 1  and the MBT (MBT 1 ) under the first correction condition having a higher compression ratio than the reference condition are set to be on the retard side from the trace knock timing TKL and the MBT under the reference condition. This is because the combustion speed and the self-ignition reaction speed are accelerated by increasing the temperature and pressure of the gas in the cylinder of the engine  210 . 
     Further, as illustrated in  FIG.  13 C , the trace knock timing TKL 2  under the second correction condition having a higher compression ratio and a higher temperature of the cooling water than under the reference condition is set to be on the further retard side from the trace knock timing TKL 1  under the first correction condition with respect to the trace knock timing TKL under the reference condition. The MBT (MBT 2 ) under the second correction condition is set to be on the retard side with respect to the trace knock timing TKL under the reference condition similarly to the trace knock timing TKL 1  under the first correction condition. This is because the influence of the cooling water temperature on the acceleration of the self-ignition reaction speed is greatly exerted on the acceleration effect of the combustion speed. 
     As described above, the influence of the rotation speed RS, the load L, and another specific variable V of the engine  210  on the correction amount of the control amount CV of the engine  210  exhibits nonlinearity having an interaction effect that changes depending on the variable V. Therefore, in the calculation of the correction amount of the control amount CV of the engine  210 , it is important to appropriately classify into a portion that can be calculated by the linear sum of the correction amounts and a portion that requires calculation by a nonlinear function in consideration of the interaction. 
     Here, as illustrated in  FIG.  2    and  FIGS.  3 A to  3 C , in the internal combustion engine control device  100  of the present embodiment, the neural network model  110  is a multilayer neural network model including the input layer Li, the intermediate layer Lm, and the output layer Lo. At least the rotation speed RS, the load L, and the specific variable V are set in each unit of the input layer Li, the weight w, the bias b, and the activation function y=f (x) are set in each unit of the intermediate layer Lm, and the control amount CV is set in the unit of the output layer Lo. In addition, in the internal combustion engine control device  100  of the present embodiment, in the neural network model  110 , for example, the logistic function f_log is set as the activation function y=f (x) of the intermediate layer Lm, and the linear function f_lin is set as the activation function y=f (x) of the output layer Lo. 
     With this configuration, the input/output relationship can be approximated by setting the operating state of the engine  210  to the input layer Li, setting the ignition timing such as MBT and trace knock to the output layer Lo, and performing supervised machine learning on the weight w and the bias b of each neuron. Therefore, according to the internal combustion engine control device  100  of the present embodiment, a large number of correction maps  120   a ,  120   b ,  120   c ,  120   d , . . . , and so on as illustrated in  FIG.  12    are not required, and it is possible to achieve a trade-off relationship between miniaturization and simplification of the internal combustion engine control device  100  and high accuracy of the control amount CV. 
     In addition, as illustrated in  FIGS.  2 ,  5 A to  5 C,  6 , and  7   , the internal combustion engine control device  100  of the present embodiment includes the determination neural network model  130  which is a logistic regression type neural network model that receives at least the rotation speed RS, the load L, and the specific variable V as inputs and outputs an index indicating whether or not the rotation speed RS, the load L, and the specific variable V are within the range of the learning condition of the neural network model  110 . For example, a logistic function l_log is set as the activation function y=f(x) of the intermediate layer Lm and the output layer Lo of the determination neural network model  130 . Then, when the determination neural network model  130  outputs an index indicating that the value is out of the range of the learning condition, the value of the unit constituting the final layer of the intermediate layer Lm of the neural network model  110  is limited to the range of the upper and lower limit values based on the maximum value Max and the minimum value Min of each unit. 
     As described above, in a case where the value is outside the region (within the extrapolation region EA) of the interpolation region IA which is the learning region of the neural network model  110 , the internal combustion engine control device  100  of the present embodiment can consider that the value is outside the range of permission for use of the neural network model  110 , and can perform the upper and lower limit processing on the output of the neural network model  110 . Therefore, according to the internal combustion engine control device  100  of the present embodiment, the control amount CV that is the ignition timing of the engine  210  can be appropriately corrected. 
     Note that the internal combustion engine control device  100  of the present embodiment may include, for example, a diagnosis unit that outputs a diagnosis result when the neural network model  110  is executed. The diagnosis unit diagnoses the neural network model  110  based on the comparison between the value of the unit of the intermediate layer Lm of the neural network model  110  and the maximum value and the minimum value of each unit of the intermediate layer Lm. 
     In addition, as described above, the internal combustion engine control device  100  of the present embodiment includes the determination neural network model  130  which is a logistic regression type neural network model that receives at least the rotation speed RS, the load L, and the specific variable V as inputs and outputs an index indicating whether or not the rotation speed RS, the load L, and the specific variable V are within the range of the learning condition of the neural network model  110 . In this case, the internal combustion engine control device  100  of the first embodiment may execute learning of the determination neural network model  130  at the time of learning of the neural network model  110 . 
     As described above, according to the present embodiment, it is possible to provide the internal combustion engine control device  100  capable of reducing the control error of the ignition timing as compared with the conventional technique. 
     Second Embodiment 
     Hereinafter, a second embodiment of an internal combustion engine according to the present disclosure will be described with reference to  FIGS.  1 ,  3 A to  3 C,  4  to  6 , and  8  to  11   , and  FIGS.  14  to  17   . 
       FIG.  14    is a schematic diagram illustrating the second embodiment of the internal combustion engine control device according to the present disclosure. An internal combustion engine control device  100 A of the present embodiment is different from the internal combustion engine control device  100  according to the first embodiment described above mainly in that the number of neural network models  110  is different and a neural network model learning unit  140  is included instead of the determination neural network model  130 . Since the other points of the internal combustion engine control device  100 A of the present embodiment are similar to those of the internal combustion engine control device  100  according to the first embodiment described above, the same reference numerals are given to the same parts, and the description thereof will be omitted. 
     The internal combustion engine control device  100 A of the present embodiment includes, for example, two or more sets of first neural network models  111 A and  111 B and second neural network models  112 A and  112 B. 
     For example, the first neural network model  111 A receives the output of the knock sensor  217  or an in-cylinder pressure sensor (not illustrated) as the reference value VrA of the rotation speed RS, the load L, and the specific variable V as an input, and outputs the ignition timing which is the control amount CV as an output OUT 1 A. In addition, for example, the second neural network model  112 A receives the output of the knock sensor  217  or the in-cylinder pressure sensor (not illustrated) as the current value VpA of the rotation speed RS, the load L, and the specific variable V as an input, and outputs the ignition timing which is the control amount CV as an output OUT 2 A. 
     The other first neural network model  111 B receives, for example, the output of the in-cylinder pressure sensor (not illustrated) or the crank angle sensor  218  as the reference value VrB of the rotation speed RS, the load L, and the specific variable V as an input, and outputs the ignition timing which is the control amount CV as an output OUT 1 B. In addition, the other second neural network model  112 B receives, for example, the output of the in-cylinder pressure sensor (not illustrated) or the crank angle sensor  218  as the current value VpB of the rotation speed RS, the load L, and the specific variable V as an input, and outputs the ignition timing which is the control amount CV as an output OUT 2 B. 
     The internal combustion engine control device  100 A calculates a difference ΔOUTA between the output OUT 1 A of the first neural network model  111 A and the output OUT 2 A of the second neural network model  112 A as a first correction amount of the reference value CVr of the control amount CV which is the output of the reference map  120 . In addition, the internal combustion engine control device  100 A calculates a difference ΔOUTB between the output OUT 1 B of the first neural network model  111 B and the output OUT 2 B of the second neural network model  112 B as the second correction amount of the reference value CVr of the control amount CV which is the output of the reference map  120 . 
     For example, the internal combustion engine control device  100 A calculates the teacher data TDA of the trace knock timing which is the optimum ignition timing based on the current value VpA of the specific variable V which is the output of the knock sensor  217  or the in-cylinder pressure sensor of the engine  210  which is the internal combustion engine. Further, the internal combustion engine control device  100 A calculates the teacher data TDB of the MBT which is the optimum ignition timing based on the current value VpB of the specific variable V which is the output of the in-cylinder pressure sensor or the crank angle sensor  218  of the engine  210 , for example. 
     The neural network model learning unit  140  includes, for example, a model selection unit  141  and a model learning unit  142 . For example, the model selection unit  141  uses the absolute value of the difference ΔOUTA between the output OUT 1 A of the first neural network model  111 A and the output OUT 2 A of the second neural network model  112 A as an input. In addition, for example, the model selection unit  141  uses the absolute value of the difference ΔOUTB between the output OUT 1 B of the first neural network model  111 B and the output OUT 2 B of the second neural network model  112 B as an input. Then, the model selection unit  141  specifies the neural network model  110  that causes an error in the control amount CV or calculates the contribution of each neural network model  110  based on the input absolute value of the difference ΔOUTA and absolute value of the difference ΔOUTB. 
     For example, the model learning unit  142  sets at least the rotation speed RS, the load L, and the current value VpA of the specific variable V as input variables, and sets the trace knock timing as the teacher data TDA as an output variable. In this way, for example, when the learning permission flag F is ON, the model learning unit  142  learns the weight w and the bias b of the neural network model  110  selected by the model selection unit  141 , for example, the first neural network model  111 A and the second neural network model  112 A. 
     In addition, for example, the model learning unit  142  sets at least the rotation speed RS, the load L, and the current value VpB of the specific variable V as input variables, and sets MBT as the teacher data TDB as output variables. In this way, the model learning unit  142  learns the weight w and the bias b of the neural network model  110  selected by the model selection unit  141 , for example, the first neural network model  111 B and the second neural network model  112 B. 
     Note that, as a learning algorithm of the model learning unit  142 , for example, an error backpropagation method can be applied. The neural network model learning unit  140  reflects, for example, the weight w and the bias b, which are parameters of the neural network model  110  learned by the backpropagation method, in the neural network model  110  selected by the model selection unit  141 . 
     Here, an example of a method of acquiring the teacher data TDB of the MBT by the internal combustion engine control device  100  will be described with reference to  FIGS.  15 A and  15 B .  FIG.  15 A  is an enlarged view of the periphery of the intake manifold  207  and the engine  210  illustrated in  FIG.  1   . The upper graph in  FIG.  15 B  is a graph in which the horizontal axis is the crank angle and the vertical axis is the in-cylinder pressure, and the lower graph in  FIG.  15 B  is a graph in which the horizontal axis is the crank angle and the vertical axis is the heat generation rate. 
     Various physical quantities related to the combustion timing of the engine  210  can be calculated based on the pressure in the cylinder of the engine  210  with respect to the crank angle, that is, the in-cylinder pressure. As illustrated in  FIG.  15 B , the timing at which the in-cylinder pressure becomes the maximum can be defined as the information of the in-cylinder pressure, and when the ignition timing is set so that the timing is, for example, about 13 degrees after the top dead center TDC, the thermal efficiency shows the maximum value. 
     For example, when the timing θ 1  at which the in-cylinder pressure measured by the in-cylinder pressure sensor  217   a  is the maximum is on the advance side of the target timing θt with respect to the current ignition timing, it is considered that an error occurs on the advance side with respect to the true value of the MBT. In this case, the MBT to which the error Δθ is added is set as the teacher data TDB. The teacher data TDB can be set with a similar idea for the error on the retard side. 
     Examples of the physical quantity related to the combustion timing of the engine  210  other than the timing when the in-cylinder pressure becomes maximum include a combustion mass ratio timing such as a combustion mass 50% timing and a combustion mass 90% timing, a heat generation rate peak timing, and an instantaneous torque peak timing. The heat generation rate and the combustion mass ratio can be calculated by a heat generation rate calculation formula expressed by the following expression (3) based on the detection value of the in-cylinder pressure sensor  217   a  and the in-cylinder volume, that is, the volume of the cylinder of the engine  210 . In Equation (3), V is the volume of the cylinder, k is the specific heat ratio, p is the pressure in the cylinder (in-cylinder pressure), and θ is the crank angle. The instantaneous torque peak timing can be derived from the temporal change behavior of the crank angle sensor. 
     
       
         
           
             
               
                 
                   
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     Next, with reference to  FIGS.  16 A to  16 C , an example of a method for acquiring the teacher data TDA of the trace knock timing by the internal combustion engine control device  100 A will be described. 
     When the teaching data TDA of the trace knock timing is acquired using the output of the knock sensor  217  illustrated in  FIG.  1   , the vibration of the cylinder block of the engine  210  due to knocking is detected based on the output of the knock sensor  217 . Since the vibration of the cylinder block includes a signal component other than knocking caused by the operation of the fuel injection valve  215  and the operations of the intake valve  211  and the exhaust valve  212 , a detection window is set. 
       FIG.  16 A  is a graph illustrating a method of acquiring the teacher data TDA of the trace knock timing using the in-cylinder pressure sensor  217   a  illustrated in  FIG.  15 A . When the teacher data TDA of the trace knock timing is acquired using the output of the in-cylinder pressure sensor  217   a , in order to detect an abnormal waveform WA due to knock, the output of the in-cylinder pressure sensor  217   a  is processed by a high-pass filter for removing a normal combustion waveform as illustrated in  FIG.  16 B . 
     Next, signal processing such as fast Fourier transform is performed on the output of the knock sensor  217  in the detection window or the output of the in-cylinder pressure sensor  217   a  subjected to the high-pass filter processing. In this way, as illustrated in  FIG.  16 C , it is possible to calculate power for each frequency with respect to the output of the knock sensor  217  or the output of the in-cylinder pressure sensor  217   a , and to calculate presence of occurrence of knock and knock intensity from an integrated value of power of frequencies related to a knock vibration component. 
     Finally, based on the relationship of the retard amount RET of the ignition timing according to the knock intensity as shown in, for example,  FIG.  16 D , it is possible to know at which timing a true trace knock exists with respect to the current ignition timing. The parameters of the neural network model  110  can be learned using the true trace knock timing as the teacher data TDA. 
     Hereinafter, the operation of the internal combustion engine control device  100 A of the present embodiment will be described with reference to  FIG.  17   . 
       FIG.  17    is a flowchart illustrating a processing flow of the internal combustion engine control device  100 A of the present embodiment. 
     First, in process P 1 , the internal combustion engine control device  100 A detects the combustion state of the engine  210  controlled based on the control amount CV output by the internal combustion engine control device  100 A based on the outputs of various sensors constituting the engine system  200 . 
     In process P 1 , examples of the sensor that detects the combustion state of the engine  210  include the in-cylinder pressure sensor  217   a , the knock sensor  217 , and the crank angle sensor  218 . Examples of the combustion state of the engine  210  include the maximum in-cylinder pressure timing, which is the timing when the pressure in the cylinder becomes maximum, and the knock strength. 
     Next, in process P 2 , the internal combustion engine control device  100 A determines the occurrence of an error of the control amount CV. In process P 2 , for example, the internal combustion engine control device  100 A determines that an error has occurred (YES) in a case where a numerical value indicating the combustion state of the engine  210  indicates a deviation state in which the numerical value is equal to or larger than a predetermined threshold value set in advance, and executes process P 3 . 
     On the other hand, in process P 2 , when the internal combustion engine control device  100 A determines that no error has occurred (NO), process P 9  to be described later is executed. 
     In process P 3 , the internal combustion engine control device  100 A determines whether or not it is a condition for performing learning. For example, when the engine system  200  is in a transient state, it is assumed that learning of the neural network model  110  cannot be normally performed. Therefore, in process P 3 , for example, when it is determined that the engine system  200  is in the transient state, the internal combustion engine control device  100 A determines that the learning of the neural network model  110  is prohibited (NO), and executes process P 9  to be described later. On the other hand, in process P 3 , when it is determined that the engine system  200  is in the steady state, for example, the internal combustion engine control device  100 A determines that the learning of the neural network model  110  is permitted (YES), and executes process P 4 . 
     In process P 4 , the internal combustion engine control device  100 A calculates teacher data TDA and TDB set to the output layer Lo of the neural network model  110 . As the teacher data TDA of the trace knock timing, as illustrated in  FIG.  16 D  described above, the target ignition timing et in which the retard amount RET, which is the retard amount of the ignition timing with respect to the knock intensity, is added to the current trace knock timing can be used. 
     In addition, as the teacher data TDB of the MBT, a value obtained by adding the difference between the current maximum in-cylinder pressure timing and the target maximum in-cylinder pressure timing, that is, the error Δθ between the timing el at which the in-cylinder pressure measured by the in-cylinder pressure sensor  217   a  illustrated in  FIG.  15 B  becomes the maximum and the target timing θt to the current MBT can be used. As a method of detecting the true MBT, for example, a combustion mass ratio timing such as a combustion mass 50% timing and a combustion mass 90% timing, a heat generation rate peak timing, an instantaneous torque peak timing, and the like can also be used. 
     Next, in process P 5 , the internal combustion engine control device  100 A specifies the neural network model  110  in which an error occurs in the ignition timing, which is the control amount CV, based on the magnitude relationship between the absolute values of the differences ΔOUTA and ΔOUTB, which are the correction amounts, or calculates the degree of contribution by the model selection unit  141  illustrated in  FIG.  14   , for example. 
     Next, in process P 6  to process P 8 , the internal combustion engine control device  100 A executes on-board learning for the neural network model  110  by the neural network model learning unit  140 . The rotation speed RS, the load L (filling efficiency), and the reference values VrA and VrB of the specific variable V are set as teacher data in the input layer Li of the neural network model  110 , and the true MBT and the trace knock timing based on the detection value of the sensor are set in the output layer Lo of the same model. As a learning algorithm of the neural network model  110 , an error backpropagation method is applied. The neural network model learning unit  140  reflects the parameters (weight w and bias b) of the neural network model  110  learned by the backpropagation method in the neural network model  110  and executes process P 9 . 
     In process P 9 , the internal combustion engine control device  100 A sets the latest parameters in each neural network model  110  constituting the ignition timing control model. In this way, by on-board learning the parameters of the neural network model  110  based on the outputs of the sensors constituting the engine system  200 , it is possible to appropriately correct a steady control error caused by temporal changes in the characteristics or individual variations of the engine  210  and the engine system  200 . 
     Next, in process P 10 , the internal combustion engine control device  100 A diagnoses the degree of time degradation of the characteristics of the internal combustion engine, that is, the engine  210  or the engine system  200  based on the result of on-board learning of the parameters of the neural network model  110  using the output of the sensor. For example, in a case where the MBT is learned to be advanced from a predetermined value, it can be regarded as an abnormal state in which the combustion speed is more slowed than expected or a sign leading to an abnormal state. Therefore, the internal combustion engine control device  100 A outputs a diagnosis result for notifying the sign. In addition, in a case where the trace knock timing is learned to be retarded from a predetermined value, it can be regarded as an abnormal state in which occurrence of knock becomes more remarkable than expected or a sign leading to an abnormal state. Therefore, the internal combustion engine control device  100 A outputs a diagnosis result for notifying the sign. 
     As described above, according to the present embodiment, similarly to the first embodiment described above, it is possible to provide the internal combustion engine control device  100 A capable of reducing the control error of the ignition timing as compared with the conventional technique. 
     In addition, the internal combustion engine control device  100 A of the present embodiment includes the neural network model learning unit  140  for learning the weight w and the bias b of the neural network model  110 . Furthermore, the internal combustion engine control device  100 A calculates the teacher data TDA of the trace knock timing which is the optimum ignition timing based on the output of the knock sensor  217  or the in-cylinder pressure sensor  217   a  of the engine  210  which is the internal combustion engine. Then, the neural network model learning unit  140  sets at least the rotation speed RS, the load L, and the specific variable Vas input variables, and sets the trace knock timing as the teacher data TDA as an output variable. 
     In addition, the internal combustion engine control device  100 A of the present embodiment includes the neural network model learning unit  140  for learning the weight w and the bias b of the neural network model  110 . Furthermore, the internal combustion engine control device  100 A calculates the teacher data TDB of the MBT which is the optimum ignition timing based on the output of the in-cylinder pressure sensor  217   a  or the crank angle sensor  218  of the engine  210  which is the internal combustion engine. Then, the neural network model learning unit  140  sets at least the rotation speed RS, the load L, and the specific variable V as input variables and sets the MBT as the teacher data TDB as an output variable. 
     In addition, in the internal combustion engine control device  100 A of the present embodiment, when the trace knock timing is learned to be retarded from the threshold value, the neural network model learning unit  140  diagnoses an abnormal state or a sign leading to an abnormality and outputs a diagnosis result. In addition, in the internal combustion engine control device  100 A according to the present embodiment, when the MBT is learned to be advanced from the threshold value, the neural network model learning unit  140  diagnoses an abnormal state or a sign leading to an abnormality and outputs a diagnosis result. Furthermore, in the internal combustion engine control device  100 A of the present embodiment, the neural network model learning unit  140  determines whether or not the engine  210  which is an internal combustion engine or the engine system  200  is in a transient state, and prohibits learning when it is determined that the engine is in the transient state. 
     With the above configuration, the combustion state when the engine  210  as the internal combustion engine is controlled according to the control amount CV of the current ignition timing can be detected by the sensors such as the in-cylinder pressure sensor  217   a , the knock sensor  217 , and the crank angle sensor  218 . In this way, the degree of deviation between the current control amount CV and the true control state can be indirectly detected. In addition, by providing the neural network model  110  for each specific variable V which is a correction parameter and selecting the neural network model  110  to be learned on the basis of the magnitude relationship between the absolute values of the differences ΔOUTA and ΔOUTB which are correction amounts, it is possible to reduce the arithmetic load required for learning. 
     In addition, the internal combustion engine control device  100 A of the present embodiment may include, for example, the neural network model learning unit  140  that selects the neural network model  110  to be learned on the basis of the correction amount and executes learning in a case where the difference between the output of the sensor that detects the combustion state of the engine  210  that is the internal combustion engine and the reference value of the control amount CV corrected by the difference ΔOUTA and the difference ΔOUTB that are the correction amounts is equal to or larger than a threshold value. 
     Although the embodiments of the internal combustion engine control device according to the present disclosure have been described in detail with reference to the drawings, the specific configuration is not limited to these embodiments, and modifications in design and the like without departing from the gist of the present disclosure are also included in the present disclosure. 
     For example, in the above-described embodiment, the optimum ignition timing (MBT timing or trace knock timing) is set to the output of the neural network model, but the internal combustion engine control device according to the present disclosure is not limited thereto. For example, the torque, the exhaust gas temperature, and the exhaust gas composition may be set as the output of the neural network model, and the internal combustion engine control device can be used as estimation means. 
     REFERENCE SIGNS LIST 
     
         
           100  internal combustion engine control device 
           100 A internal combustion engine control device 
           110  neural network model 
           111  first neural network model 
           111 A first neural network model 
           111 B first neural network model 
           112  second neural network model 
           112 A second neural network model 
           112 B second neural network model 
           130  determination neural network model (logistic regression type neural network model) 
           140  neural network model learning unit 
           200  engine system (internal combustion engine) 
           209  flow enhancing valve 
           210  engine (internal combustion engine) 
           217  knock sensor 
           217   a  in-cylinder pressure sensor 
           218  crank angle sensor 
           219  variable compression ratio mechanism 
         b bias 
         CV control amount 
         CVr reference value of control amount 
         f_log logistic function 
         f_lin linear function 
         L load 
         Li input layer 
         Lm intermediate layer 
         Lo output layer 
         Max maximum value 
         Min minimum value 
         RS rotation speed 
         R_OUT ratio (correction amount) 
         TDA teacher data of trace knock timing 
         TDB MBT teacher data 
         V specific variable 
         Vr reference value of specific variable 
         Vp current value of specific variable 
         w weight 
         y=f(x) activation function 
         ΔOUT difference (correction amount) 
         ΔOUTA difference (correction amount) 
         ΔOUTB difference (correction amount)