learning processing system

Abstract: The present invention is concerned with a learning processing system adapted to cause the signal processing section (50) formed by a neural network (L I , L H , L O ) to undergo signal processing pursuant to the back-propagation learning rule, wherein the local minimum state in the course of the learning processing may be avoided by learning the coefficient of coupling strength while simultaneously increasing the number of the unit (u H1 to u Hy ) of the intermediate layer (L H ).

Description: BACKGROUND OF THE INVENTION Field of the invention This invention relates to a signal processing apparatus or system carrying out signal processing with the use of a so-called neural network made up of a plurality of units each taking charge of signal processing corresponding to that of a neuron, and a learning processing apparatus or system causing a signal processing section by said neural network to undergo a learning processing in accordance with the learning rule of back propagation. Prior art The learning rule of back propagation, which is a learning algorithm of the neural network, has been tentatively applied to signal processing, including high speed image processing or pattern recognition, as disclosed in "Parallel Distributed Processing", vol. 1, The MIT Press, 1986 or "Nikkei Electronics", issue of August 10, 1987, N° 427, pp 115 to 124. The learning rule of back propagation is also applied, as shown in Fig. 1, to a multistorey neural network having an intermediate layer 2 between an input layer 1 and an output layer 3. Each unit u j of the neural network shown in Fig. 1 issues an output value which is the total sum net j of output values O i of a unit u j coupled to the unit u j by a coupling coefficient W ji , transformed by a predetermined function f, such as a sigmoid function. That is, when the value of a pattern p is supplied as an input value to each unit u j of the input layer 1, an output value O pj of each unit u j of the intermediate layer 2 and the output layer 3 is expressed by the following formula (1) O pj = f j (net pj ) = f j (Σ i W ji• O pji ) The output value O pj of the unit u j of the output layer 3 may be obtained by sequentially computing the output values of the inputs u j , each corresponding to a neuron, from the input layer 1 towards the output layer 3. In accordance with the back-propagation learning algorithm, the processing of learning consisting in modifying the coupling coefficient W ji so as to minimize the total sum E p of square errors between the actual output value O pj of each unit u j of the output layer 3 on application of the pattern p and the desirable output value t pj , that is the teacher signal, is sequentially performed from the output layer 3 towards the input layer 1. By such processing of learning, the output value O pj closest to the value t pj of the teacher signal is output from the unit u j of the output layer 3. If the variant Δ W ji of the coupling coefficient W ji which minimizes the total sum E p of the square errors is set so that Δ W ji α - ∂ Ep/∂W ji the formula (3) may be rewritten to Δ Wji = η . δ pj O pj as explained in detail in the above reference materials. In the above formula (4), η stands for the rate of learning, which is a constant, and which may be empirically determined from the number of the units or layers or from the input or output values. δ pj stands for the error proper to the unit u j . Therefore, in determining the above variant ΔW ji , it suffices to compute the error δ pj in the reverse direction, or from the output layer towards the input layer of the network. The error δ pj of the unit u j of the output layer 1 is given by the formula (5) δ pj =(t pj - O pj )f' j (net j ) On the other hand, the error δ pj of the unit u j of the intermediate layer 2 may be computed by a recurrent function of the following formula (6) using the error δ pk and the coupling coefficient W kj of each unit u k coupled to the unit u j , herein each unit of the output layer 3. The process of finding the above formulas (5) and (6) is explained in detail in the above reference materials. In the above formulas, f' j (net j ) stands for the differentiation of the output function f j (net j ). Although the variant W ji may be found from the above formula (4), using the results of the formulas (5) and (6), more stable results may be obtained by finding it from the following formula (7) Δ W ji (n+1)=η.δ pj O pi + α.Δ W ji(n) with the use of the results of the preceding learning. In the above formula, α stands for a stabilization factor for reducing the error oscillations and accelerating the convergence thereof. The above described learning is repeated until it is terminated at the time point when the total sum E p of the square errors between the output value O pj and the teacher signal t pj becomes sufficiently small. It is noted that, in the conventional signal processing system in which the aforementioned back-propagation learning rule is applied to the neural network, the learning constant is empirically determined from the numbers of the layers and the units corresponding to neurons or the input and output values, and the learning is carried out at the constant learning rate using the above formula (7). Thus the number of times of repetition n of the learning until the total sum E p between the output value O pj and the teacher signal t pj becomes small enough to terminate the learning may be enormous to render the efficient learning unfeasible. Also, the above described signal processing system is constructed as a network consisting only of feedforward couplings between the units corresponding to the neurons, so that, when the features of the input signal pattern are to be extracted by learning the coupling state of the above mentioned network from the input signals and the teacher signal, it is difficult to extract the sequential time series pattern or chronological pattern of the audio signals fluctuating on the time axis. In addition, while the processing of learning of the above described multistorey neural network in accordance with the back-propagation learning rule has a promisingly high functional ability, it may occur frequently that an optimum global minimum is not reached, but only a local minimum is reached, in the course of the learning process, such that the total sum E p of the square errors cannot be reduced sufficiently. Conventionally, when such local minimum is reached, the initial value or the learning rate η is changed and the processing of learning is repeated until finding the optimum global minimum. This results in considerable fluctuations and protractions of the learning processing time. The paper by Kung et al entitled "An Algebraic Projection Analysis for Optimal Hidden Units Size and Learning Rates in Back-Propagation Learning" (IEEE International Conference on Neural Networks, San Diego, California, July 24-27, 1988) seeks to optimize a learning process consisting of iterative application of the back propagation algorithm and uses an approach whereby it is sought to minimise the value of a modified measure of mean squared error. The paper by Koutsougeras et al entitled "Training of a Neural Network for Pattern Classification Based on an Entropy Measure" (IEEE International Conference on Neural Networks, San Diego, California, July 24-27, 1988) discusses a neural network having a branching structure designed to partition n-dimensional space into different discrete regions corresponding to respective different classes of patterns. The branching structure is incrementally built up during a training phase where input patterns are applied to the individual neurons of the network and threshold and weight values of the neurons are adjusted starting from the input layer and proceeding towards the output laser. Extra neurons are added as needed in order adequately to partition the n-dimensional space. Objects of the invention It is a primary object of the present invention to provide a learning processing system in which the signal processing section of the neural network is subjected to learning processing in accordance with a back-propagation learning rule, wherein the local minimum state in the learning processing process may be efficiently avoided for realizing an optimum global minimum state quickly and stably. Summary of the invention For accomplishing the primary object of the present invention, the present invention provides a learning processing system in which the learning processing section executes the learning processing of the coupling strength coefficient as it increases the number of units of the intermediate layer. The above and other objects and novel features of the present invention will become apparent from the following detailed description of the invention which is made in conjunction with the accompanying drawings and the new matter pointed out in the claims. BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a diagrammatic view showing the general construction of a neural network to which the back-propagation learning rule is applied. Fig. 2 is a block diagram schematically showing the construction of an illustrative example of a signal processing system. Fig. 3 is a diagrammatic view of a neural network showing the construction of the signal processing section of the signal processing system according to the system shown in Fig. 2. Fig. 4 is a flow chart showing the process of learning processing in the learning processing section constituting the signal processing system of the system shown in Fig. 2. Fig. 5 is a block diagram schematically showing the construction of the learning processing system according to the present invention. Figs. 6A and 6B are diagrammatic views showing the state of the signal processing section at the start and in the course of learning processing in the learning processing system shown in Fig. 5. Fig. 7 is a flow chart showing a typical process of learning processing in the learning processing section constituting the learning processing system shown in Fig. 5. Fig. 8 is a chart showing the typical results of tests of learning processing on the signal processing section of the neural network shown in Fig. 5 by the learning processing section of the learning processing system. Fig. 9 is a chart showing the results of tests of learning on the signal processing section of the neural network shown in Fig. 3, with the number of units of the intermediate layer fixed at six. Fig. 10 is a chart showing the results of tests of learning on the signal processing system of the neural network shown in Fig. 3, with the number of units of the intermediate layer fixed at three. DETAILED DESCRIPTION OF THE EMBODIMENTS By referring to the drawings, certain preferred embodiments of the present invention will be explained in more detail. An illustrative example of signal processing system will be hereinafter explained. As shown schematically in Fig. 2, the signal processing system of the present illustrative example includes a signal processing section 30 for obtaining the output value O pj from the input signal patterns p and a learning processing section 40 for causing the signal processing section 30 to undergo learning to obtain the output value O pj closest to the desired output value t pj from the input signal patterns p. The signal processing section 30 is formed, as shown in Fig. 3, by a neural network of a three-layer structure including at least an input layer L I , an intermediate layer L H and an output layer L O . These layers L I , L H and L O are constituted by units u I1 to u ix , u H1 to u Hy , and u O1 to u Oz , each corresponding to a neuron, respectively, where x, y and z stand for arbitrary members. Each of the units u H1 to n Hy and u O1 to u Oz of the intermediate layer L H and the output layer L O is provided with delay means and forms a recurrent network including a loop LP having its output O j(t) as its own input by way of the delay means and a feedback FB having its output O j(t) as an input to another unit. In the signal processing system 30, with the input signal patterns p entered into each of the units u I1 to u Ix of the input layer L I , the total sum netj of the inputs to the units u H1 to u Hy of the intermediate layer L H is given by the following formula (17): Each of the units u H1 to u Hy of the intermediate layer L H issues, for the total sum net j of the input signals, an output value O Hj(t) represented by the sigmoid function of the following formula (18): O Hj(t) = 1 1+e -netj The total sum net j of the inputs to the units u O1 to u Oz of the output layer L O is given by the following formula (19): While each of the units u O1 to u Oz of the output layer L O issues, for the total sum net j of the inputs, an output value O oj(t) represented by the following formula (20): O oj (t) = 1 1+e -netj where O j stands for a threshold value and NI, NH and NO stand for the numbers of the delay means provided in the layers L I , L H and L O , respectively. The learning processing section 40 computes the coefficient W ji of coupling strength between the units u O1 to u Oz , u H1 to u Hy and u I1 to u Ix , from the output layer L O towards the input layer L I , sequentially and repeatedly, according to the sequence shown in the flow chart of Fig. 4, while executing the learning processing of the coupling coefficient W ji so that the total sum of the square errors LMS between the desired output value t pj afforded as the teacher signal and the output value O oj of the output layer L O will be sufficiently small. By such learning processing, the learning processing section 40 causes the output value O oj of the output layer L O to be closest to the desired output value t zr , afforded as the teacher signal patterns, for an input signal pattern p (xr) supplied to the signal processing section 30. This pattern p (xr) represents an information unit a whole which fluctuates along the time axis and represented by the xr number of data, where r stands for the number of times of sampling of the information unit and x the number date in each sample. That is, the section 40 affords at step 1 the input signal patterns p (xr) to each of the units u I1 to u Ix of the input layer L I , and proceeds to computing at step 2 each output value O pj(t) of each of the units u H1 to u Hy and u O1 to u Oz of the intermediate layer L H and the output layer L O . The section 40 then proceeds to computing at step 3 the error δ pj of each of the units u O1 to u Oz and u H1 to u Hy , from the output layer L O towards the input layer L I , on the basis of the output values O pj(t) and the desired output value t zr afforded as the teacher signal. In the computing step 3, the error δ oj of each of the units u O1 to u Oz of the output layer L O is given by the following formula (21): δ oj = (t pj - O oj )O oj (1 - O oj ) wherein the error δ pj of each of the units u H1 to u Hy of the intermediate layer L H is given by the following formula (22) δ Hj = O Hj (1 - O Hj ) Σ k δ ok W kj Then, in step 4, the learning variable β j of the coefficient W ji of coupling strength from the i'th one to the j'th one of the units u I1 to u Ix , u H1 to u Hy and u O1 to u Oz is computed by the following formula (23) in which the learning variable β j is represented by the reciprocal of the square sum of the input values added to by 1 as a threshold value. Then, in step 5, using the learning variable β j computed in step 4, the variant Δw ij of the coupling coefficient W ji from the i'th one to the j'th one of the units u O1 to u Oz , u H1 to u Hy and u I1 to u Ix is computed in accordance with the following formula (24): ΔW ji(n) = η.β(δ pj O pi ) In the formula, η stands for a learning constant. Then, in step 5, the total sum LMS of the square errors on the units with respect to the teacher signal is computed in accordance with the formula (25): Then, in step 6, it is decided whether the processing of the steps 1 through 5 has been performed on the R-number of input signal patterns p xr . If the result of decision at step 6 is NO, the section 40 reverts to step 1. When the result of decision at step 6 is YES, that is, when all of the variants Δ W ji of the coupling coefficient w ji between the units u O1 to u O2 , u H1 to u y and u I1 to U Ix are computed for the input signal patterns p xr , the section 40 proceeds to step 7 to execute decision of the converging condition for the output value O oj obtained at the output layer L O on the basis of the total sum LMS of square errors between the output value O oj and the desired output value t pj afforded as the teacher signal. In the decision step 7, it is decided whether the output value O oj obtained at the output layer L O of the signal processing section 30 is closest to the desired output value t pj afforded as the teacher signal. When the result of decision at step 7 is YES, that is, when the total sum LMS of the square errors is sufficiently small and the output value O oj is closest to the desired output value t pj , the learning processing is terminated. If the result of decision at step 7 is NO, the section 40 proceeds to computing at step 8. In this computing step 8, the coupling coefficient W ji between the units u O1 to u Oz , u H1 to u Hy and u I1 to u Ix is modified, on the basis of the variant W ji of the coupling coefficient W ji computed at step 5, in accordance with the following formula (26) ΔW ji(n) =ΔW ji(n) +αΔW ji(n-1) and the following formula (27) W ji(n+1) =W ji(n) + Δ Wji(n) After the computing step 8, the section 40 reverts to step 1 to execute the operation of steps 1 to 6. Thus the section 40 executes the operations of the steps 1 to 8 repeatedly and, when the total sum LMS of the square errors between the desired output value t pj and the actual output value O oj becomes sufficiently small and the output value O oj obtained at the output value L O of the signal processing section 30 is closest to the desired output value t pj afforded as the teacher signal, terminates the processing of learning by the decision at step 7. In this manner, in the illustrative example signal processing system, the learning as to the coupling coefficient W ji between the units u O1 to u Oz , u H1 to u Hy and u I1 to u Ix of the signal processing section 30 constituting the recurrent network inclusive of the above mentioned loop LP and the feedback FB is executed by the learning processing section 40 on the basis of the desired output value t pj afforded as the teacher signal. Hence, the features of the sequential time-base input signal pattern p xr , such as audio signals, fluctuating along the time axis, may also be extracted reliably by the learning processing by the learning processing section 40. Thus, by setting the coupling state between the units u O1 to u Oz , u H1 to u Hy and u I1 to u Ix of the signal processing section 30 by the coupling coefficient W ji , obtained as the result of learning by the learning processing section 40, the time-series input signal pattern p xr can be subjected to desired signal processing by the signal processing section 30. Moreover, in the illustrative example system, the learning constant η is normalized by the learning constant β indicated as the reciprocal of the square sum of the input values at the units u H1 to u Hy and u O1 to u Oz , and the learning processing as to the coupling coefficient W ji is performed at the dynamically changing learning rate, as a function of the input value O pi , so that learning can be performed stably and expeditiously with a small number of times of learning. In this manner, in the illustrative example signal processing system, signal processing for input signals is performed at the signal processing section 30 in which the recurrent network inclusive of the loop LP and the feedback FB is constituted by the units u H1 to u Hy and u O1 to u Oz of the intermediate layer L H and the output layer L O each provided with delay means. In the learning processing section 40, the learning as to the coupling state of the recurrent network by the units u H1 to u Hy and u O1 to u Oz constituting the signal processing section 30 is executed on the basis of the teacher signal. Thus the features of the sequential time-base patterns, fluctuating along the time axis, such as audio signals, can be extracted by the above mentioned learning processing section to subject the signal processing section to the desired signal processing. A preferred illustrative embodiment learning processing system according to the present invention will be hereinafter explained. The basic construction of the learning processing system according to the present invention is shown in Fig. 5. As shown therein, the system includes a signal processing section 50 constituted by a neural network of a three-layered structure including at least an input layer L I , an intermediate layer L H and an output layer L O , each made up of plural units performing a signal processing corresponding to one of a neuron, and a learning processing section 60 subjecting the learning processing to the signal processing consisting in sequentially repeatedly computing the coefficient W ji of coupling strength between the above units from the output layer L O towards the input layer L I on the basis of the error data δ pj between the output value of the output layer L O and the desired output value O pj afforded as the teacher signal t pj , for the input signal patterns p entered into the input layer L I of the signal processing section 50, and learning the coupling coefficient W ji in accordance with the back-propagation learning rule. The learning processing section 60 executes the learning processing of the coupling coefficient W ji as it causes the number of the units of the intermediate layer L H of the signal processing section 50 to be increased, and thus the section 60 has the control function of causing the number of units of the intermediate layer L H to be increased in the course of learning processing of the coupling coefficient W ji . The learning processing section 60 subjects the signal processing section 50 having the input layer L I , an intermediate layer L H and an output layer L O made up of arbitrary numbers x, y and z of units u I1 to u Ix , u H1 to u Hy and u O1 to u Oz , each corresponding to a neuron, respectively, as shown in Fig. 6A, to learning processing as to the coupling coefficient W ji , while the section 60 causes the number of the unit L H to be increased sequentially from y to (y+m), as shown in Fig. 6B. It is noted that the control operation of increasing the number of the units of the intermediate layer L H may be performed periodically in the course of learning processing of the coupling coefficient W ji , or each time the occurrence of the above mentioned local minimum state is sensed. The above mentioned learning processing section 60, having the control function of increasing the number of the units of the intermediate layer L H in the course of learning processing of the coupling coefficient W ji , subjects the signal processing section 50 formed by a neural network of a three-layer structure including the input layer L I , intermediate layer L H and the output layer L O to the learning processing of the coupling coefficient W ji , as it causes the number of units of the intermediate layer L H to be increased. Thus, even on occurrence of the local minimum state in the course of learning of the coupling coefficient W ji , the section 50 is able to increase the number of units of the intermediate layer L H to exit from such local minimum state to effect rapid and reliable convergence into the optimum global minimum state. Tests were conducted repeatedly, in each of which the learning processing section 50 having the control function of increasing the number of units of the intermediate layer in the course of learning of the coupling coefficient W ji causes the signal processing section 60 constituting the recurrent network including the feedback FB and the loop LP in the illustrative example, signal processing system of Figs. 2-4 to undergo the process of learning the coefficient W ji , with the number of the units of the input layer L I of 8(x=8), that of the output layer L O of 3(z=3), the number of the delay means of each layer of 2 and with the input signal pattern p during learning operation, using 21 time-space patterns of 1=8x7, and the processing algorithm shown in the flow chart of Fig. 7, with the learning being started at the number of the units of the intermediate layer L H of 3(y=3) and with the number of the units of the intermediate layer L H being increased during the learning process. By increasing the number of the units of the intermediate layer L H three to five times, the test results were obtained in which the convergence to the optimum global minimum state were realized without going into the local minimum state. Fig. 8 shows, as an example of the above tests, the test results in which learning processing of converging into the optimum minimum state could be achieved by adding the units of the intermediate layer L H at the timing shown by the arrow mark in the figure and by increasing the number of the intermediate layer L H from three to six. The ordinate in Fig. 8 stands for the total sum LMS of the quadratic errors and the abscissa stands for the number of times of the learning processing operations. The processing algorithm shown in the flow chart of Fig. 7 is explained. In this processing algorithm, in step 1, the variable K indicating the number of times of the processing for detecting the local minimum state is initialized to "0", while the first variable Lms for deciding the converging condition of the learning processing is also initialized to 10000000000. Then, in step 2, the variable n indicating the number of times of learning of the overall learning pattern, that is, the l -number of the input signal patterns p , is initialized. The program then proceeds to step 3 to execute the learning processing of the l -number of the input signal patterns p . Then, in step 4, decision is made of the variable n indicating the number of times of learning. Unless n=3, the program proceeds to step 5 to add one to n (n → n+1), and then reverts to step 3 to repeat the learning processing. When n=3, the program proceeds to step 6. In step 6, after the value of the first variable Lms is maintained as the value of the second variable Lms (-1) for deciding the converging condition of the learning processing, the total sum of the square errors between the output signal and the teacher signal in each unit is computed in accordance with the formula (28), this value being then used as the new value for the first variable Lms, such that Then, in step 7, the first variable Lms for deciding the converging condition of the learning processing is compared with the second variable Lms(-1). If the value of the first variable Lms is lesser than that of the second variable Lms(-1), the program proceeds to step 8 to decide whether or not the variable k indicating the number of times of the processing operations for detecting the local minimum state is equal to 0. If, in step 8, the variable k is 0, the program reverts directly to step 2. If the variable k is not 0, setting of k→k-1 is made in step 9. The program then reverts to step 2 to initialize n to 0(n=0) to execute the learning processing of the l -number of the input signal patterns p in step 3. If, in step 7, the value of the first variable Lms is larger than that of the second variable Lms(-1), the program proceeds to step 10 to set the value of k indicating the number of times of the processing operations for detecting the local minimum state (k → k + 1). Then, in step 11, it is decided whether or not the value of k is 2. If, in step 11, the value of the variable k is not 2, the program reverts directly to step 2. If the variable k is 2, it is decided that the local minimum state is prevailing. Thus, in step 12, control is made of increasing the number of the units of the intermediate layer L H . Then, in step 13, setting of k=0 is made. The program then reverts to step 2 for setting of n=0 and then proceeds to step 3 to execute the learning processing of the above mentioned l -number of the input signal patterns p. Test on the learning processing was conducted of the signal processing section 50 of the above described illustrative example signal processing system of Figs. 2-4 constituting the recurrent network including the feedback loop FB and the loop LP shown in Fig. 3, with the number of the units of the intermediate layer L H being set to six (y=6). The test results have revealed that the learning processing need be repeated an extremely large number of times with considerable time expenditure until the convergence to the optimum minimum state was achieved, and that the local minimum state prevailed for three out of eight learning processing tests without convergence to the optimum global minimum state. Fig. 9 shows, by way of an example, the results of the learning processing tests in which the local minimum state was reached. In this figure, the ordinate stands for the total sum LMS of the square errors and the abscissa stands for the number of times of the learning processing operations. Also the tests on the learning processing was conducted 30 times on the signal processing section 50 of the above described illustrative example signal processing system constituting the recurrent network including the feedback loop FB and the loop LP shown in Fig. 3, with the number of the units of the intermediate layer L H being set to three (y=3). It was found that, as shown for example in Fig. 10, the local minimum state was reached in all of the tests on learning processing without convergence to the optimum global minimum state. In Fig. 10, the ordinate stands for the total sum LMS of the square errors and the abscissa stands for the number of times of the learning processing operations. From the foregoing it is seen that the present invention provides a learning processing system in which the learning processing of the coefficient of coupling strength is performed, while the number of the units of the intermediate layer is increased by the learning processing section, whereby the convergence to the optimum global minimum state is achieved promptly and reliably to achieve the stable learning processing to avoid the local minimum state in the learning processing process conforming to the back-propagation learning rule.

Claims: A learning processing system (50, 60) comprising: a signal processing section (50) composed of a multi-layer neural network having an input layer (L I ), a bidden layer (L H ) and an output layer (L O ), the layers being made up of units u I1 to u Ix , u H1 to u Hy and u O1 to u Oz , respectively, each unit corresponding to a neuron; and a learning processing section (60) executing a learning process using a back-propagation learning algorithm, the process consisting in sequentially modifying, from the output layer towards the input layer, the coupling coefficients W ji of all units j in the hidden and in the output layer by a variant ΔW ji so as to minimize the total sum of square errors between the actual output O pj of unit j in the output layer (L O ) produced from an input signal pattern (p) and the desirable output value t pj (teacher signal) for said unit j in the output layer (L O ), whereby W ji is the weight for the signal from the ith to the jth unit, the learning processing section being fed with a desired output value t pj as a teacher signal for the output value O pj of the unit j in the output layer (L O ) for the input patterns p entered into the input layer (L I ), the learning processing section (60) computing the error value for each unit in the output layer and in the bidden layer, said learning process being executed repeatedly until the total sum (E) of the square error between the desired output afforded as the teacher signal and the output signal becomes sufficiently small; characterised in that the learning processing section (60) comprises control means for increasing the number of units in the bidden layer (L H ), during the repeated execution of said learning process, either periodically or when a local minimum state of the signal processing system has been detected, said learning processing section (GO) being adapted in subsequent repeated executions of the learning process to perform learning processing of the coefficients W ji of coupling strength in respect of the increased number of units in the hidden layer. The learning processing system of claim 1, wherein the control means of the learning processing section (60) is adapted to detect a local minimum state by comparing successive values of a first variable, Lms, where

Inventors: HIRAIWA ATSUNOBU, HIRAIWA, ATSUNOBU
Applicants: SONY CORP, SONY CORPORATION
Application year: 1989
Main classification: G06F15
IPC codes: G06N3