JP2559882B2 - Fuzzy controller - Google Patents

Description

DETAILED DESCRIPTION [Overview] A fuzzy controller that calculates and outputs a control operation amount corresponding to a control state amount according to a fuzzy control rule, and realizes an arithmetic function of an algebraic sum as a consequent operation of a fuzzy control rule. At the same time, for the purpose of easily enabling the change to the logical OR operation that has been used conventionally, the consequent operation means is composed of an input unit for distributing the input signal value, an input from the preceding layer and A plurality of input units are used as input units with a basic unit that receives an internal state value to be multiplied with respect to an input to obtain a product sum value and obtains an output by function-converting the product sum value. age,
And one or more basic units as an intermediate layer and one or more intermediate layers, and one basic unit as an output layer, between the input layer and the frontmost intermediate layer, between the intermediate layers, and An inner coupling is formed between the intermediate layer and the output layer in the final stage, and a value that will output the algebraic sum value of the input signal values from the output layer is set as the internal state value of the inner coupling. It is composed of a hierarchical network structure part.

[Industrial applications]

The present invention relates to a fuzzy controller that calculates and outputs a control operation amount corresponding to a control state amount by executing fuzzy inference according to a fuzzy control rule, and particularly as a consequent part operation described in the fuzzy control rule. The present invention relates to a fuzzy controller that realizes an arithmetic function of algebraic sum and can easily change to a conventionally used logical sum operation.

Fuzzy control is spreading as a new control processing method. In this fuzzy control, a control algorithm including ambiguity such as human judgment is expressed in if-then format, and this control algorithm is executed according to fuzzy inference, whereby the control operation amount is detected from the detected control state amount. It calculates and controls the controlled object. In order to absorb the ambiguity of the control algorithm, the fuzzy controller that realizes this fuzzy control needs to be configured so that the arithmetic function described in the control algorithm can be easily changed.

[Conventional technology]

The fuzzy control rule is of the form if x ₁ is big and x ₂ is small then y ₁ is big (the IF part is called the antecedent part and is the part that describes the conditions for the control state quantity such as temperature data. , THEN part is called the consequent part, which is the part that describes the condition of the control operation amount such as the operation end), and the fuzzy controller describes the control logic of the controlled object. It manages such multiple fuzzy control rules, and quantifies the meaning of ambiguous linguistic expressions such as “large” and “small” described in each fuzzy control rule as a membership function. It will take a configuration to manage.

When a fuzzy controller receives a control state quantity such as temperature data or water level data from a control target, first, a membership function of the control state quantity being managed (becomes a membership function of the antecedent part). The membership function value (truth value) of the control state quantity given by Next, in accordance with the antecedent operation of selecting the minimum value, the process of determining the applied value to the antecedent in each fuzzy control rule is executed. That is, to explain using the example of the fuzzy control rules above, "x ₁ is big"
The truth value of "0.8" and the truth value of "x ₂ is small" are "0.
In the case of 5 ", processing is performed so as to determine this" 0.5 "as the applicable value to the consequent part of the fuzzy control rule in accordance with the antecedent part calculation.

Then, the fuzzy controller follows each fuzzy control rule given for the same membership function of the same control operation amount (becomes the consequent membership function) according to the consequent operation of selecting the maximum value. A process of determining an applicable value for the membership function from the applied value for the subject part is executed. That is, in the fuzzy control rule described above, “0.5” is applied to the consequent part “y ₁ is big”.
When "0.6" is applied to "y ₁ is big" by another fuzzy control rule by using the fuzzy control rule, this "0.6" is used as the control value y according to the consequent operation. It is processed so as to be determined as the applied value to the membership function of "big" of ₁ .

Subsequently, the fuzzy controller reduces the membership function of the control operation amount according to the determined applied value, and obtains the center of gravity of the function sum of the reduced membership functions for the same control operation amount, etc. In accordance with the process (1), a process of calculating a control operation amount which is a fuzzy inference value and outputting it to the operation end or the like is executed. That is, the membership function of "big" of the control operation amount y ₁ is reduced according to the determined applied value, and the membership function such as "small" of the control operation amount y ₁ obtained from another fuzzy control rule is reduced to The control value is reduced according to the determined application value, and the control operation amount that is the output as the fuzzy controller is calculated according to the processing such as obtaining the barycenter of the figure of the function sum of the membership functions.

In the fuzzy controller having such a configuration, conventionally, as described above, the fuzzy controller has a consequent part arithmetic function for executing an arithmetic process of selecting and outputting the maximum value of the applicable values to be processed. Was configured as follows. Then, the consequent part arithmetic function is configured to be implemented by a program means.

[Problems to be Solved by the Invention] However, the fuzzy control rule is generated according to the knowledge of the target process of the operator who is familiar with the control of the control target. In the future, it is not possible to generate a fuzzy control rule that can achieve the desired control from the beginning, and the fuzzy control rule generated will be tuned by trial and error while being evaluated by simulations and field tests. Therefore, there is no choice but to take the procedure of completing the fuzzy control rule suitable for the controlled object.

From now on, the consequent part arithmetic function is not the logical sum operation of selecting and outputting the maximum value of the applied values to be processed,
It happens that another kind of arithmetic function is needed.
However, since the conventional fuzzy controller has only the consequent operation function of the logical sum operation, it has a problem that it cannot meet such a demand. In order to deal with this demand, it is possible to deal with the necessary consequent part calculation function as a subroutine by preparing it in the device in advance. However, if such a correspondence method is adopted, the memory capacity will be squeezed, etc. Another problem will come up.

The present invention has been made in view of such circumstances,
While experimenting with the arithmetic function of algebraic sum as a consequent part operation described in the fuzzy control rule, a new fuzzy controller that can easily realize the change to the logical sum operation used conventionally It is intended to be provided.

[Means for solving the problem]

 FIG. 1 is a block diagram showing the principle of the present invention.

In the figure, 10 is a fuzzy controller equipped with the present invention, 11 is a fuzzy rule management unit that manages fuzzy control rules that describe the control logic for a control target, 12
Is a fuzzy rule selection unit that sequentially selects the fuzzy control rules managed by the fuzzy rule management unit 11 to be processed. 13 is a membership function management unit of the antecedent part. It manages the truth value of the antecedent part membership function that quantifies the linguistic expression about the described control state quantity, and 14 is the consequent part membership function management part, which is described in the fuzzy control rule. That manages the truth value of the consequent part membership function that quantifies the linguistic expression regarding the controlled operation amount,
Reference numeral 15 is an input data acceptance unit that executes acceptance processing of the control state quantity data collected from the controlled object, and 16 is an antecedent part truth value calculation unit that processes the selected fuzzy control rule as a processing unit. , Which calculates the truth value of the antecedent membership function of the input control state quantity,
Reference numeral 17 denotes an antecedent part operation part, which is an applied value to the consequent part of the fuzzy control rule to be processed by performing an antecedent part operation on the calculated truth value of the antecedent part membership function. 18 is the consequent part arithmetic part, and from the applied value to the consequent part of each fuzzy control rule given for the same consequent part membership function, to the subsequent part membership function. That determines the applicable value for
Reference numeral 19 denotes an inference value calculation unit, which applies the fuzzy control rules that do not have the same consequent part output from the antecedent part calculation part 17 and the same consequent part output from the consequent part calculation part 18. With the applied values of the fuzzy control rules having and, for example, the consequent membership function is reduced according to those applied values, and the figure centroid of the functional sum of the contracted consequent membership functions is calculated. The control manipulated variable data, which is a fuzzy inference value, is calculated by obtaining it.

The consequent operation unit 18 of the present invention receives one or a plurality of inputs and an internal state value to be multiplied by the inputs to obtain a product sum value, and performs a function conversion of the product sum value. A hierarchical network structure unit 20 configured to have an output value by a hierarchical network structure of a plurality of basic units 1 and performing a consequent operation, and an internal state assigned to the internal connection of the hierarchical network structure unit 20. It is composed of an internal state value management unit 21 that manages values.
This hierarchical network structure unit 20 has a basic unit 1 and an input unit 1'which distributes an input signal value as a structural unit, and an input layer composed of a plurality of input units 1'-h and one or a plurality of input layers. An input layer 1'-h and a basic unit 1-i are provided, which are provided with an intermediate layer composed of the basic unit 1-i and provided in one or a plurality of stages, and an output layer composed of one basic unit 1-j. , Between the basic units 1-i, and the basic unit 1-i
A hierarchical network structure in which i and the basic unit 1-j are internally connected to each other is adopted. And the internal state value management unit
Reference numeral 21 has a configuration for managing the value of the internal state value that the hierarchical network structure unit 20 operates to output the algebraic sum value of the input signal values.

[Action]

The hierarchical network structure unit 20 of the present invention inputs the input to the input layer according to the data conversion function defined by its own hierarchical network structure and the internal state value assigned to the internal connection of the hierarchical network structure. It operates so as to calculate the algebraic sum value of the signal values and output it to the inference value calculation unit 19. That is, z ₁ , z ₂ , ..., z _n as input values
When (0 ≦ z _i ≦ 1) is input, the following Yager
The algebraic sum value Y defined by setting p = 1.5 in the equation (1) is calculated and output.

Y = z ₁ ∨z ₂ ∨ ... ∨z _n = ₁ ∧ (z ₁ ^p + z ₂ ^p + ... + z _n ^p ) ^{1 / P} As described above, the consequent operation part 18 of the present invention operates the antecedent operation. When the applied value of each fuzzy control rule for the same consequent part membership function is input as an input signal from part 17, the algebraic sum value of the input signal values that are input is calculated and Since it is processed so as to be determined as the applied value to the department membership function, it is more suitable for the case where the consequent part arithmetic function that executes the conventional OR operation cannot perform appropriate control. It will give a way to realize appropriate control.

The data conversion function of the hierarchical network structure unit 20 is characterized in that even if the hierarchical network structure is the same, the content of the operation can be freely changed by changing the value of the internal state value assigned to the internal connection. Therefore, by registering the value of the internal state value that operates to output the logical sum of the input signal values in the internal state value management unit 21, it is possible to immediately return to the same configuration as the conventional fuzzy controller. Of.

〔Example〕

 Hereinafter, the present invention will be described in detail according to examples.

FIG. 2 shows an embodiment of the present invention. As described with reference to FIG. 1, reference numeral 17 in the drawing denotes an antecedent part operation part, 18 an antecedent part operation part, 20 a hierarchical network structure part, and 21 an internal state value management part.

The antecedent part computing part 17 processes so as to calculate the application value of each fuzzy control rule for the same consequent part membership function. That is, for example, when a plurality of fuzzy control rules prepared from rule 1 to rule n describe “Small” of the control operation amount Y ₁ as the description of the consequent part, the antecedent part computation unit 17 , As shown in FIG.
Control operation amounts Y ₁ defined by rule 1 and application value Z ₁ with respect to the membership function of "Small", control operation amounts Y ₁ defined by rule 2 to the membership function of the "Small" and applying value Z ₂ of Te, as such applicable value Z _n with respect to the membership function of "Small" control operation amounts Y ₁ defined from the rule n, "Small" control operation amounts Y ₁
Performs processing to calculate the application value of each fuzzy control rule for the membership function of. In FIG. 2, for the sake of convenience of description, the membership function values (truth values) are shown in parallel.
You will be asked one by one.

The hierarchical network structure unit 20 performs the consequent part calculation on the applied value given from the antecedent part calculation part 17,
Processing will be performed so as to determine the applicable value for the consequent membership function to be processed.

FIG. 3 illustrates the basic configuration of the basic unit 1 that constitutes the hierarchical network structure unit 20. As shown in this figure, the basic unit 1 is a multi-input / single-output system, in which a plurality of inputs are assigned weight values of internal coupling (corresponding to the internal state values described in FIG. 1). A multiplication processing unit 2 for multiplying, an accumulation processing unit 3 for adding all the multiplication results thereof, and a threshold processing unit 4 for subjecting the accumulated value to nonlinear threshold processing and outputting one final output. When the h layer is the front layer and the i layer is the rear layer, the accumulation processing unit 3 of the i-th basic unit 1 of the i layer executes the operation of the following equation (1) and the threshold processing unit In step 4, processing is performed so as to execute the operation of the following expression (2).

y _pi = 1 / (1 + exp (−x _pi + θ _i )) (2) where, h: unit number of layer p: input signal pattern number θ _i : threshold value of unit i of layer W _ih : h -Weight value of internal coupling between the i layers y _ph : output from the h-th unit of the h-layer with respect to the input signal of the p-th pattern Then, the hierarchical network structure unit 20 uses the basic unit 1 and the antecedent unit having such a configuration. With the input unit 1 ′ that distributes the applicable value calculated by the calculation unit 17 as a structural unit,
As shown in FIG. 4, an input layer composed of a plurality of input units 1'-h and an intermediate layer composed of one or a plurality of basic units 1-i and provided in one or more stages ( (1 stage in this embodiment) and one basic unit 1
An output layer constituted by -j, and between the input unit 1'-h and the basic unit 1-i, between the basic units 1-i, and between the basic unit 1-i and the basic unit 1-. It is configured to have a hierarchical network structure configured by internally coupling j and j. Here, the number of units of the input units 1′-h is the consequent part membership function that is most often described when the consequent part calculation part 18 is implemented by one hierarchical network structure part 20. It will be prepared according to the number of fuzzy control rules to be described.

The hierarchical network structure unit 20 configured in this way is
Demonstrating the function of executing processing of converting an input signal that is input to a corresponding output signal according to a data conversion function that is defined by a hierarchical network structure and a weight value assigned to the internal connection of the hierarchical network structure. That is, processing is performed so that the consequent operation is executed according to this data conversion function. In the present invention, according to the data conversion function of the hierarchical network structure unit 20, the consequent operation unit 18 is configured to calculate and output an algebraic sum value of input signal values.

The hierarchical network structure unit 20 can be configured by either program means or hardware means. However, when it is configured by hardware means, the applicant has filed “Japanese Patent Application No. 63-216865 ( It is possible to use the one disclosed in “Network configuration data processing device” filed on August 31, 1988.

That is, the basic unit 1 is, as shown in FIG.
Multiplication type for multiplying the output from the preceding layer input via the input switch unit 7 and the weight value held by the weight value holding unit 8
The addition result of the D / A converter 2a, the analog adder 3a that adds the output value of the multiplication D / A converter 2a and the previous accumulated value to calculate a new accumulated value, and the analog adder 3a A sample-hold circuit 3b for holding, a non-linear function generating circuit 4a for performing non-linear conversion of the held data of the sample-hold circuit 3b when the accumulation processing is completed, and an analog of the non-linear function generating circuit 4a which is an output to a subsequent layer. It is realized by including the output holding unit 5 that holds the signal value, the output switch unit 6 that outputs the held data of the output holding unit 5, and the control circuit 9 that controls each of these processing units.

The hierarchical network structure unit 20 is realized by a structure in which the basic unit 1 having this structure is electrically connected by a single common analog bus 70 as shown in FIG. Here, in the figure, 71 is a weight output circuit for giving a weight value to the weight value holding unit 8 of the basic unit 1, 72 is an initial signal output circuit corresponding to the input unit 1 ', and 73 is a synchronization signal which is a control signal for data transfer. A synchronous control signal line for transmitting the control signal to the weight output circuit 71, the initial signal output circuit 72 and the control circuit 9, and a main control circuit 74 for transmitting the synchronous control signal.

In the hierarchical network structure unit 20 of this configuration, the main control circuit 74 sequentially selects the base unit 1 of the preceding layer in time series and, in synchronization with this selection processing, the output holding unit of the selected base unit 1. The final output held by 5 is processed to be output to the multiplication type D / A converter 2a of the basic unit 1 of the subsequent layer via the analog bus 70 according to the time-division transmission format. Upon receiving this input, the multiplication type D / A converter 2a of the basic unit 1 in the subsequent layer sequentially selects the corresponding weight values and performs the multiplication processing of the input value and the weight value, and the analog adder 3a and the sample hold. The accumulation processing unit 3 including the circuit 3b sequentially accumulates the multiplied values. Then, when all the accumulation processing for the basic unit 1 of the previous layer is completed, the main control circuit 74 activates the nonlinear function generating circuit 4a of the basic unit 1 of the subsequent layer to calculate the final output, The output holding unit 5 performs processing so as to hold the final output of this conversion processing result. Then, the main control circuit 74 repeats the same processing for the next succeeding layer by using this latter layer as a new preceding layer, and thereby the output pattern (output signal) corresponding to the input pattern (input signal).
Is processed so as to be output.

The hierarchical network structure unit 20 is characterized in that even if the hierarchical network structure is fixed, the data conversion function can be set according to the weight value assigned to the internal connection of the hierarchical network structure. In the present invention, by utilizing this feature, the consequent operation unit 18 is configured to execute an algebraic sum operation.

FIG. 7 shows an embodiment of a learning system for learning the weight value assigned to the internal connection of the hierarchical network structure unit 20. Next, the learning process for determining the weight value of the internal connection of the hierarchical network structure unit 20 will be described in detail according to the embodiment of the learning system.

In FIG. 7, reference numeral 30 is a hierarchical network simulator built on a computer, which simulates the data processing function of the hierarchical network structure unit 20, and 40 is a learning signal presentation device, which is a hierarchical network structure unit 20. Manages a learning signal group (a basic unit is a pair of a learning presentation signal and a learning teacher signal) used for learning the weight value of the internal coupling of, and further, the learning presentation signal in the learning signal group is hierarchically arranged. The learning teacher signal is presented to the network simulator 30 and the learning processing device 50 to be described later. In order to realize this processing, the learning signal presentation device 40
A learning signal storage unit 41 that manages a learning signal group, and a learning signal for learning is read from the learning signal storage unit 41, and the learning presentation signal in the learning signal is presented to the hierarchical network simulator 30, and another pair is formed. Learning teaching signal of the learning processing device 50 described later, a learning signal presentation unit 42 presenting the learning convergence determination unit 43 described below, an output signal output from the hierarchical network simulator 30, and a learning signal presentation unit 42.
The learning signal is received from the learning signal presenting unit 42, and it is determined whether the error of the data processing function of the hierarchical network simulator 30 is within the allowable range.
And a learning convergence determination unit 43 for notifying the.

Reference numeral 50 denotes a learning processing device that implements a back propagation method known as a weight value learning algorithm of a hierarchical network configuration data processing device, and is output in response to the presentation of the learning presentation signal by the learning signal presentation device 40. The error value between the output signal group from the hierarchical network simulator 30 and the learning teacher signal group presented from the learning signal presentation device 40 is calculated, and the weight value to be learned based on the error value. Is sequentially updated to learn the weight value of the internal coupling within which the error value is within the allowable range.

That is, the learning processing device 50 uses the h-layer-i-layer-3 shown in FIG. 4 according to the back propagation method.
If explained in the layered hierarchical network structure section 20,
An output signal y _pj from the output layer that is output when a learning input signal (learning presentation signal) is presented, and the output signal y
_When the learning teacher signal d _pj , which is the signal to be taken by _pj , is determined, first, the difference value [d _pj −y _pj ] between the output signal y _pj and the learning teacher signal d _pj is calculated, and then α _pj = y _pj (1-y _pj ) (d _pj -y _pj ) is calculated, and then According to the update amount ΔW _ji (t) of the weight value between the i layer and the j layer
Is calculated. Here, t represents the number of times of learning, and the reason for adding the value related to the update amount of the weight value determined in the previous update cycle is to speed up the learning.

Subsequently, the learning processing device 50 uses the calculated α _pj ,
First of all, And then According to the update amount ΔW _ih (t) of the weight value between the h layer and the i layer
Is calculated. Subsequently, according to the calculated update amount, the weight value W _ji (t) = W _ji (t−1) + ΔW _ji (t) W _ih (t) = W _ih (t−1) + ΔW for the next update cycle. By repeating the method of determining _ih (t), it is the output signal y _Pj from the output layer that is output when the learning presentation signal is presented, and the output signal y _Pj that should be taken. The weight values W _ji and W _ih and the threshold values θ _i and θ _j that will be in agreement with the learning teacher signal d _Pj are learned.

In addition, the applicant of the present invention, as disclosed in the previous application “Japanese Patent Application No. 62-333484 (filed on Dec. 28, 1987,“ network configuration data processing device ”)”, has the h-layer on the input side. Proposed to incorporate threshold value θ into weight value W and treat threshold value θ as a weight value by providing a unit that always outputs “1” and assigns threshold value θ as a weight value to the output. did. From this, the learning of the threshold value θ is also learned by the same learning process as the learning of the weight value W.

As shown in FIG. 7, the hierarchical network simulator 30 includes an input unit 1'of the hierarchical network structure unit 20.
And a network structure management unit 31 that manages the internal connection relationship of the basic unit 1, an operation control unit 32 that controls the entire arithmetic processing, an input unit 1 ′ of the hierarchical network structure unit 20 and each unit of the basic unit 1. An input data expansion unit 33 that temporarily expands input input values, and a weight value management unit 34 that manages the weight values assigned to each inner join.
And an arithmetic execution unit 35 provided to simulate the arithmetic function of the basic unit 1, and an output data management unit 36 for managing the arithmetic result of the arithmetic execution unit 35. And
The calculation execution unit 35 includes a multiplication value calculation unit 37 that calculates a multiplication value of a weight value and an input value, a sum total value calculation unit 38 that calculates a sum value of the calculation values of the multiplication value calculation unit 37, and a sum total of these sum values. Value calculator
It is configured to include a threshold value calculation unit 39 that performs threshold processing on the calculated value of 38.

With such a configuration, the hierarchical network simulator 30 simulates the data processing function executed by the hierarchical network structure unit 20 installed in the fuzzy controller. When the hierarchical network structure unit 20 is configured by a program means, it can be realized by using such a hierarchical network simulator 30, for example.

In order to construct the hierarchical network structure unit 20 to execute the algebraic sum operation, the user first generates the input / output signal relationship of the algebraic sum operation to generate the learning signal presentation signal 40.
The process of registering in the learning signal storage unit 41 is executed. That is, the input signal and the input / output signal relationship in which the algebraic sum value of these input signals is used as the output signal are generated and registered in the learning signal storage unit 41.

Algebraic sum value Y for _two values z ₁ and z ₂ (0 ≦ z _i ≦ 1)
Is expressed by Y = z ₁ + z ₂ −z ₁ · z ₂ . From this, FIG. 8 shows the input / output signal relationship of this algebraic sum operation when the number of input units 1'-h in the input layer of the hierarchical network structure unit 20 is two. In the example of FIG. 8, an example in which 81 input / output signal relationships are generated is shown.

Subsequently, the user activates the learning signal presenting device 40 by instructing to use the input signal in the registered input / output signal relationship as the learning presentation signal and the output signal associated with this as the learning teacher signal. At the same time, by starting the learning processing device 50 in correspondence with this starting process, the weight value of the weight value management unit 34 of the hierarchical network simulator 30 is set to start learning so as to realize this input / output signal relationship. Give instructions.

In accordance with this activation instruction, the hierarchical network simulator 30 operates as the hierarchical network structure unit 20 implemented (or scheduled to be implemented) in the fuzzy controller to convert the presented learning presentation signal into the data conversion specified by the weight value. The learning processing device 50 converts this according to the function and outputs it.
Upon receiving the output signal from 30, the internal connection weight value (managed by the weight value management unit 34) is updated according to the back propagation method described above, and the learning signal presentation device 40 receives the output from the hierarchical network simulator 30. The learning signal is repeatedly presented until the output signal substantially matches the learning teacher signal. By this processing, the weight value of the internal connection of the hierarchical network structure unit 20 that realizes the registered input / output signal relationship of the algebraic sum operation is stored in the weight value management unit 34 of the hierarchical network simulator 30.

FIG. 9 shows learning data when learning is executed using the input / output signal relationship shown in FIG. 8 as a learning signal. Here, FIG. 9 (a) is output signal data of the hierarchical network simulator 30 for presentation of each learning presentation signal when the number of times of learning is 0 (that is, at the start of learning), FIG. 9 (b). Is the output signal data of the hierarchical network simulator 30 for the presentation of each learning presentation signal when the number of learning is 1000, and in FIG. 9 (c), the number of learning is 34.
The output signal data of the hierarchical network simulator 30 for the presentation of each learning presentation signal at 07 times is shown. Here, this learning was performed when the intermediate layer of the hierarchical network structure unit 20 was composed of 10 units of one-stage structure.
Further, at the start of learning, a random number value generated by the random number generating means is assigned as an initial value of the weight value (including the threshold value) of each internal connection. In this figure, for example, when the learning presentation signal “E06” of the learning signal of FIG. 8 is given to the hierarchical network simulator 30, when the number of learning times is 3407, “0.822” is output from the hierarchical network simulator 30. It means that it was done. Here, "0,800" that is associated with "0.822" in parentheses is the learning teacher signal of "E06".

FIG. 10 shows a plot of the number of learning times and the error of the data processing function of the hierarchical network simulator 30. As shown in this figure, when the number of learning times exceeds 1,000, in response to the presentation of the learning signal shown in FIG. 8, the hierarchical network simulator 30 outputs a learning teacher signal roughly corresponding thereto. Then, in this state, even if something that is not included in the learning signal is presented, an operation is performed to output an appropriate output signal that is close to the content of the algebraic sum operation.

FIG. 11 shows the learning value of the weight value and threshold value (managed by the weight value management unit 34 of the hierarchical network simulator 30) assigned to each internal connection of the hierarchical network structure unit 20 when the number of learning times is 3,407. Illustrate. here,
"4.052063" is the first unit of the input layer and the first unit of the middle layer.
The weight value of the inner coupling with the unit, “2.188287” is
The weight value of the internal coupling between the unit of the output layer and the first unit of the intermediate layer, “−2.968790” represents the threshold value of the first unit of the intermediate layer, and “−1.387681” represents the threshold value of the unit of the output layer. There is.

As can be seen from FIG. 9 (c) and FIG.
If the weight values and threshold values shown in the figure are registered in the internal state value management unit 21 of the hierarchical network structure unit 20 implemented in the fuzzy controller, the consequent operation unit for executing the arithmetic processing by the hierarchical network structure unit 20. 18 executes the consequent part calculation of calculating and outputting the algebraic sum value of the applied values calculated by the antecedent part calculating part 17.

Due to the execution of the algebraic sum operation processing executed by the consequent operation part 18, there is a case where the consequent operation of the logical sum operation used by the conventional fuzzy controller may not perform appropriate control. Will also provide a way to enable more appropriate control.

On the other hand, when the weight value and the threshold value of FIG. 13 obtained by the learning signal defining the input / output relation of the logical sum operation shown in FIG. 12 are registered in the internal state value management unit 21, the hierarchical network structure is registered. The output signal which gives the logical sum value of the input signals as shown in FIG. 14 is output from the unit 20 with respect to the input of the learning presentation signal of FIG. As described above, in the present invention, while realizing the arithmetic function of the algebraic sum operation as the consequent operation described in the fuzzy control rule, by simply changing the management data of the internal state value management unit 21,
It is also possible to return to the consequent part operation of the logical sum operation provided in the conventional fuzzy controller.

Although the illustrated embodiment has been described, the present invention is not limited to this. For example, in the embodiment, the example of application to the consequent operation unit 18 of two inputs has been described, but the present invention is not limited to two inputs and can be applied as it is to a multi-input. The arithmetic processing of the basic unit 1 is not limited to the threshold conversion processing. In addition, the applicant of the present invention has previously filed “Japanese Patent Application No. 63-227825 (filed on September 12, 1988,“ learning processing method of network configuration data processing device ”)” for the back propagation method. Although the invention for improving the learning processing of the weight value in a shorter time is disclosed, the present invention discloses such an improved back propagation method or another weight other than the back propagation method. A value learning method can also be used. The weight value and the threshold value may be learned using the hierarchical network structure unit 20 itself installed in the fuzzy controller without using a simulator or the like.

〔The invention's effect〕

As described above, according to the present invention, while realizing the arithmetic function of the algebraic sum as the consequent operation of the fuzzy control rule, the fuzzy control which is conventionally used can be easily changed. Since it becomes possible to construct a controller, it is possible to configure a fuzzy controller that is more suitable for the controlled object, and it is possible to easily deal with the case where it is necessary to operate as a conventional fuzzy controller. .

[Brief description of drawings]

1 is a block diagram of the principle of the present invention, FIG. 2 is an embodiment of the present invention, FIG. 3 is a block diagram of a basic unit, FIG. 4 is a block diagram of a hierarchical network structure unit, and FIG. An example of a basic unit, FIG. 6 is an example of a hierarchical network structure part, FIG. 7 is an explanatory diagram of a learning system used by the present invention, FIG. 8 is an explanatory diagram of a learning signal to be generated, and FIG. 10 and 10 are explanatory views of learning data, FIG. 11 is an explanatory view of learning data of weight values and threshold values, FIG. 12 is an explanatory view of learning signals generated when logical OR operations are assigned, and FIG. 13 is logical FIG. 14 is an explanatory diagram of learning data of a weight value and a threshold value of the sum calculation, and FIG. 14 is an explanatory diagram of output data when operating as a logical sum calculation function. In the figure, 1 is a basic unit, 1'is an input unit, 10 is a fuzzy controller, 16 is an antecedent part truth value calculation part, 17 is an antecedent part calculation part, 18 is an antecedent part calculation part, and 20 is a hierarchical network. A structure unit, 21 is an internal state value management unit, 30 is a hierarchical network simulator, 40 is a learning signal presentation device, and 50 is a learning processing device.

 ─────────────────────────────────────────────────── ─── Continuation of the front page (72) Inventor Asahi Kawamura 1015 Kamiodanaka, Nakahara-ku, Kawasaki City, Kanagawa Prefecture, Fujitsu Limited (72) Inventor Ryusuke Masuoka 1015, Kamedotachu, Nakahara-ku, Kawasaki, Kanagawa Prefecture Fujitsu Limited ( 72) Inventor Kazuo Asakawa 1015 Kamiodanaka, Nakahara-ku, Kawasaki City, Kanagawa Prefecture, Fujitsu Limited (72) Inventor Shigenori Matsuoka, No. 1, Fujimachi, Hino-shi, Tokyo Inside Fujifacom Control Ltd. (72) Inventor Hiroyuki Okada Tokyo Fujichocom Control Co., Ltd., Fujimachi No. 1 in Hino City (56) References JP-A-2-292602 (JP, A) JP-A-3-134704 (JP, A) JP-A-3-222003 (JP, A) International publication 89/11684 (WO, A) Kanno "Fuzzy control" (63.5.30) Nikkan Kogyo Shimbun Terano et al. Temu Getting Started "(flat 1.9.20) Ohm, Inc.

Claims (1)

(57) [Claims] Claim: What is claimed is: 1. An antecedent part truth value calculating part (16) for calculating a membership function value for an input control state quantity in units of fuzzy control rules, and an antecedent part truth value calculating part (16). To the output value for the fuzzy control rule having the same consequent part output from the antecedent part arithmetic part (17), which performs the logical operation on the calculated value of Consequent operation part (18) that performs logical operation and obtains output value
And an output value for the fuzzy control rule that does not have the same consequent part output from the antecedent part computing part (17), an output value output from the consequent part computing part (18), and a control operation amount. In the fuzzy controller provided with the inference value calculation unit (19) for calculating the control operation amount from the membership function value of the input unit (1), the consequent operation unit (18) distributes the input signal value. ′) And one or more inputs from the previous layer and an internal state value to be multiplied with the inputs to obtain a sum of products value,
A basic unit (1) that obtains an output by function-transforming the sum of products is used as a structural unit, a plurality of the input units (1′-h) is used as an input layer, and one or a plurality of the basic units (1 -I) as an intermediate layer and one or more intermediate layers, and one of the basic units (1-j) as an output layer, between the input layer and the foremost intermediate layer, and between the intermediate layers. Also, the hierarchical network structure part (20) internally coupled between the intermediate layer and the output layer at the final stage, and the area for managing the internal state value referred to by the hierarchical network structure part (20) are accessible from the outside. And an internal state value management unit (21) for managing the internal state value, which is prepared in a form and operates so that the hierarchical network structure unit (20) outputs an algebraic sum of input signal values. Fuzzy controller to do.