JP2763371B2 - Hierarchical network configuration fuzzy controller - Google Patents

Description

Description: TECHNICAL FIELD The present invention relates to a hierarchical network configuration fuzzy controller that functions as an adaptive fuzzy controller.

Fuzzy control is becoming popular as a new control processing method. In this fuzzy control, a control algorithm including ambiguity such as human judgment is expressed in an if-then form, and the control algorithm is executed in accordance with fuzzy inference. This is to calculate and control the control target. Since the fuzzy controller that executes the fuzzy control handles indefinite ones, it is necessary to adopt a configuration that can adaptively cope with adjustment and change of the control algorithm.

[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 describes the conditions for the control state quantity such as temperature data. , THEN part is called a consequent part, and is a part that describes the condition of the control operation amount such as the operation end). The fuzzy controller describes the control logic of the control object. By managing such multiple fuzzy control rules prepared as, and quantifying the meaning of ambiguous linguistic expressions such as "large" and "small" described in each fuzzy control rule as a membership function The configuration to manage is adopted.

When the fuzzy controller is given a control state quantity such as temperature data and water level data from the control object, first, a membership function of the control state quantity being managed (becomes a membership function of the antecedent part). Calculates the membership function value (truth value) of the control state quantity given from. Next, according to the antecedent operation such as selecting the minimum value,
A process for determining an applied value to the consequent part in each fuzzy control rule is executed. That is, if the truth value of “x ₁ is big” is “0.8” and the truth value of “x ₂ is small” is “0.5”, the minimum value According to the antecedent operation to select the value,
Processing is performed so as to determine “0.5” as an applied value to the consequent part of the fuzzy control rule.

Subsequently, the fuzzy controller, in accordance with the consequent part operation such as selecting the maximum value, calculates the fuzzy control rules given for the same membership function of the same control operation amount (which becomes the consequent part membership function). From the applied value to the consequent part, a process of determining an applied value to the membership function is executed. That is, the applied value "0.5" for "y ₁ IS big" of the consequent part in the above-described fuzzy control rules, on the other hand, with a different fuzzy control rules for "y ₁ IS big""0.6" In the case where is set as the applied value, the "0.6"
Than it is processed to determine the applied value of the for the "big" control operation amounts y ₁ a.

Subsequently, the fuzzy controller reduces the membership function of the control operation amount in accordance with the determined applied value, and also obtains the fuzzy center of the function sum of the membership function for the same control operation amount. A process of calculating a control operation amount which is an inference value is executed, and a process of outputting the control operation amount to an operation terminal or the like is executed.

The conventional fuzzy controller employs a configuration in which the execution of such fuzzy control rules is implemented by executing the program. That is, according to the software means of the computer system that performs the sequential processing,
First, the truth value of the control state quantity is calculated, then, the application value for the consequent part is determined according to the antecedent operation, and then the membership function of the control operation amount according to the consequent operation. , The control operation amount is calculated and output by the center of gravity calculation or the like.

[Problems to be solved by the invention]

However, the fuzzy control rules are generated in accordance with the knowledge of the target process of the operator who is familiar with the control of the control target. From now on, the fact is that it is not possible to generate fuzzy control rules that can achieve the desired control from the beginning, and we will tune it by trial and error while evaluating the generated fuzzy control rules by simulation and on-site tests. Therefore, it is necessary to take a procedure of completing a fuzzy control rule suitable for the control target.

However, in the conventional method of constructing a fuzzy controller using software means, the fuzzy controller simply executes the control logic in accordance with the description procedure of the generated fuzzy control rule and has no adaptability. is not. Therefore, the conventional fuzzy controller does not exert a function of reducing the load of tuning the fuzzy control rules for the operator.

The present invention has been made in view of such circumstances,
The provision of a hierarchical network configuration fuzzy controller that constitutes a new fuzzy controller that adopts a configuration that can adaptively respond to adjustment or change of the fuzzy control rule while adopting a configuration for realizing the fuzzy control rule. It is the purpose.

[Means for solving the problem]

 FIG. 1 is a diagram illustrating the principle of the present invention.

In the figure, reference numeral 10 denotes a hierarchical network configuration fuzzy controller according to the present invention, which adopts a configuration for realizing a fuzzy control rule and has a configuration capable of adaptively coping with adjustment and change of the fuzzy control rule. 11 is a fuzzy controller having a hierarchical network configuration.
A fuzzy control rule according to a data conversion function defined by a hierarchical network structure and an internal state value assigned to an internal connection of the hierarchical network structure, and 17 is an internal state value management unit. The hierarchical network unit 11 manages internal state values required at the time of execution of the fuzzy control rule, and 20 is a learning signal presenting device, which stores a control state amount data group for learning in the hierarchical network unit 11. A control operation amount data group for learning, which is presented to the input layer and forms a pair with the control state amount data for learning (when a control state amount data for learning is provided, a desired control state is realized. For the learning processing operation 30 described below, which is a learning processing device. Between the control operation amount data group from the hierarchical network unit 11 output in response to the presentation of the learning control state amount data group and the learning control operation amount data group presented from the learning signal presentation device 20 Is calculated, and the internal state value to be learned is sequentially updated based on the error value, thereby learning the internal state value in which the error value is within an allowable range.

The hierarchical network unit 11 includes an input layer constituted by a plurality of input units 12 provided in a format corresponding to the input control state quantity, and a layer subsequent to the input layer, which is a member of the antecedent of the fuzzy control rule. A first calculation layer constituted by a plurality of processing units 13 provided in a format corresponding to the ship function, and a plurality of, for example, a number corresponding to the number of fuzzy control rules, positioned as a subsequent layer of the first calculation layer. A first rule layer constituted by the rule units 14 and a plurality of rule units 15 which are positioned as a subsequent layer of the first rule layer and have a number corresponding to the number of membership functions of the consequent part of the fuzzy control rule, for example. A plurality of processes for calculating a function sum of a second rule layer configured and a membership function of the consequent part whose size is changed, which is positioned as a subsequent layer of the second rule layer; A second calculation layer configured by the logical unit 16a and a second calculation layer positioned as a subsequent layer of the second calculation layer are provided in association with the control operation amount as two sets for calculating the derived value of the control operation amount. The output unit 16b is configured to have a hierarchical network structure of an output layer.

The input unit 12 of the input layer receives the control state quantity of the control target as an input, and the corresponding processing unit of the first calculation layer.
The input control state quantity is distributed to 13.

The processing unit 13 of the first calculation layer receives the multiplied value of the control state amount notified from the input unit 12 and the internal state value allocated to the internal connection with the input unit 12 as a fuzzy control rule. The truth value of the antecedent membership function is calculated and output to the corresponding rule unit 14 of the first rule layer.

The rule unit 14 of the first rule layer is a processing unit
A grade value is calculated by inputting a multiplied value of a truth value notified from 13 and an internal state value assigned to an internal connection between the processing unit 13 and a corresponding rule unit in the second rule layer. Output to 15.

The rule unit 15 of the second rule layer includes the grade value notified from the rule unit 14 and the rule unit.
A grade value is calculated by using a multiplied value by an internal state value allocated to an internal connection between the input and the input as an input, and a grade value is calculated and output to the corresponding processing unit 16a of the second calculation layer.

The processing unit 16a of the second calculation layer includes the rule unit 1
The grade value notified from 5 and its rule unit 15
The value is multiplied by the grade value of the consequent part membership function divided value (discrete function value cut out from the consequent part membership function) assigned to the inner join between The processing unit 16a calculates the function sum of the consequent part membership function (each processing unit 16a calculates a partial value of the function sum, and the sum thereof becomes the function sum), and outputs the result to the corresponding output unit 16b of the output layer.

The output unit 16b of the output layer includes a function sum partial value of the consequent part membership function notified from the processing unit 16a,
A derived value used for deriving a control operation amount is obtained by taking a multiplied value by a constant division value (a numerical value obtained by partitioning between predetermined constant values) allocated to an internal connection with the processing unit 16a as an input. calculate.

A grade between the first rule layer and the second rule layer is obtained by multiplying an output value of a corresponding rule unit of the preceding rule layer by an internal state value assigned to an internal connection between the rule units. One or more rule layers composed of one or more rule units for calculating values may be provided.

[Action]

In the hierarchical network unit 11 of the present invention, when a control state quantity is given via the input unit 12, the processing unit 13 of the first calculation layer performs a function conversion function assigned to the control state quantity. To calculate the truth value of the membership function for the control state quantity that is the operation result value, and to internally combine the calculated truth value with the first.
Is distributed and output to the rule unit 14 of the rule layer. Upon receiving the truth value of the membership function for the control state quantity from the corresponding one or more processing units 13, the rule unit 14 of the first rule layer executes the arithmetic processing such as the assigned function conversion function. Thus, a grade value as an operation result value is calculated, and the calculated grade value is internally combined with the rule unit 15 of the second rule layer.
Output to Here, when the rule layers are further hierarchically provided between the first rule layer and the second rule layer, the rule units of those rule layers calculate the grade value and calculate the grade value of the second rule layer. Processing is performed so as to propagate to the rule unit 15.

When a grade value is received from one or a plurality of corresponding rule units in the preceding layer, the rule unit 15 in the second rule layer performs an operation process such as a function conversion function assigned thereto to obtain an operation result value. A grade value is calculated, and the calculated grade value is output to the processing unit 16a of the second calculation layer to be internally connected.

Upon receiving this grade value, the processing unit 16a
According to the consequent part membership function division value assigned to the inner join with the rule unit 15, a partial value of the function sum of the consequent part membership function whose size is changed by the grade value is calculated. Output unit 16b of the output layer in which the calculated function sum partial values are internally connected
Output to Then, when receiving the partial value of the function sum of the consequent part membership function, the output unit 16b generates the derived value used for deriving the control operation amount according to the constant division value allocated to the internal connection with the processing unit 16a. Is calculated.

As described above, the hierarchical network unit 11 of the present invention operates to execute the fuzzy control rules according to the data conversion function of the hierarchical network structure structured to conform to the fuzzy control rules.

In the present invention, the hierarchical network configuration fuzzy controller 10
Is to be constructed as a desired fuzzy controller, the operator first assigns to the processing unit 13 a function of calculating the truth value of the membership function for the generated control state quantity, and In the internal connection between the control unit 15 and the processing unit 16a, a membership function for the generated control operation amount is registered, and the rule units 14 and 15 (the first and second rules) of the first and second rule layers are registered. When a rule layer is provided between the layers, the rule units are also assigned based on the knowledge of the fuzzy control rules obtained for the rule units. Here, the number of rule units 14 and 15 is also set based on the number of obtained fuzzy control rules. Then, the processing unit 13 of the first calculation layer and the rule unit 14 of the first rule layer
And an internal connection between the rule unit 14 of the first rule layer and the rule unit 15 of the second rule layer in a format according to, for example, a fuzzy control rule. Initial values are set and stored in the internal state value management unit 17. Further, when a rule layer is provided between the first and second rule layers, an internal connection is formed in an appropriate format between the rule units, and an initial value of an internal state value is set. Then, processing is performed so as to be stored in the internal state value management unit 17.

Subsequently, the operator activates the learning signal presentation device 20 and the learning processing device 30. When activated in this way,
The learning signal presenting device 20 presents the learning control state amount data to the input layer of the hierarchical network unit 11 and presents the learning control operation amount data to the learning processing device 30. In response to the presentation of the learning signal, the learning processing device 30 outputs the control operation amount data from the hierarchical network unit 11 output in response to the presentation of the learning control state amount data, to the learning control amount to be presented. The processing unit 13 and the rule unit 14 should be roughly matched with the operation amount data.
And the internal state value of the internal connection between rule unit 14 and rule unit 15, and when a rule layer is provided between the first and second rule layers, In addition to this, a process of learning the internal state value of the internal connection between the rule units and setting the learned value of the obtained internal state value in the internal state value management unit 17 is performed.

In the learning process of the internal state value, the internal state value of the internal connection set between the input unit 12 and the processing unit 13 and the internal state value set between the rule unit 15 and the processing unit 16a are set. It is possible to make the internal state value of the connection a learning target.

Assuming that the internal state value of the internal connection set between the input unit 12 and the processing unit 13 is to be learned, the membership function of the control state quantity assumed in advance is further improved by a new internal state value obtained by this learning processing. Tuning can be performed to match the control target.

Further, when the internal state value of the internal connection set between the rule unit 15 and the processing unit 16a is to be learned,
With the new internal state value obtained by this learning process, it becomes possible to tune the membership function of the control operation amount assumed in advance to one that is more suitable for the control target.

When the learning value of the internal state value is set, the hierarchical network configuration fuzzy controller 10 functions as a fuzzy controller that executes a desired control logic.

As described above, the present invention is characterized in that a fuzzy controller is constructed according to the hierarchical network unit 11 having a hierarchical network structure structured to conform to the fuzzy control rules.

As is well known as a neurocomputer, a data processing device that follows a data conversion function of a hierarchical network structure, when an internal state value assigned to an internal connection of the hierarchical network structure is set, an unexpected input signal is generated. This realizes "soft" data processing of outputting an appropriate output signal even if it is input. According to the present invention, the hierarchical network structure of such a hierarchical network configuration data processing device is structured so as to be adapted to fuzzy control rules and provided as a hierarchical network unit 11. Then, the internal state value of the internal connection of the rule part of the fuzzy control rule can be learned separately from the membership function for the previously generated control state amount and control operation amount.

By using such a hierarchical network configuration data processing device, the learning processing device 30 can mechanically determine the association of the fuzzy control rules. Can be greatly reduced. And
Instead of using the hierarchical network configuration data processing device as it is, using the hierarchical network unit 11 structured in a format compatible with the fuzzy control rules, the fuzzy control rules mapped to the hierarchical network unit 11 can be called It becomes visible from the outside, the membership function can be tuned separately by the membership function, and the like, and the consistency with the conventional fuzzy controller can be maintained.

〔Example〕

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

FIG. 2 shows an embodiment for constructing the hierarchical network configuration fuzzy controller 10 of the present invention. In the figure, the same components as those described in FIG. 1 are indicated by the same symbols. 1'-h is an input unit corresponding to the input unit 12 in FIG. 1, 1-i is a basic unit constituting the processing unit 13 and rule units 14, 15 in FIG. 1, and 1-j is a first unit. The basic unit 17a constituting the output unit 16 shown in FIG.
Weight management unit corresponding to the hierarchical network unit
It manages the weight values (corresponding to the internal state values in FIG. 1) assigned to the 11 internal connections.

Reference numeral 21 denotes a learning signal storage unit, and a hierarchical network unit 11
Signal used for learning the weight value of (the basic unit is a pair of control state data and control operation data)
22 is a learning signal presenting unit, which reads out the learning signal from the learning signal storage unit 21 and presents the control state amount data to the hierarchical network unit 11 and the other of the pair The control operation amount data is presented to a weight value changing unit 30a to be described later and a learning convergence determining unit 23 described below. 23 is a learning convergence determining unit, and the control operation amount data output from the hierarchical network unit 11. Receiving the learning control operation amount data from the learning signal presenting unit 22, it is determined whether or not the error of the data processing function of the hierarchical network unit 11 is within an allowable range, and the determination result is represented by the learning signal presenting unit 22. Is to be notified.

30a is a weight value changing unit corresponding to the learning processing device 30 in FIG. 1, and receives learning control operation amount data from the learning signal presenting unit 22 and network output data from the hierarchical network unit 11, By calculating the update amount of the weight value according to the back propagation method and updating the weight value, learning is performed so that the weight value converges to the convergence value.

As described in FIG. 1, the hierarchical network unit 11 of the present invention has a hierarchical network structure structured to have the same structure as the fuzzy control rules. Hierarchical network unit 11 characteristic of the present invention
Prior to the description, a hierarchical network structure (hereinafter, referred to as a known hierarchical network structure) provided in a generally known hierarchical network configuration data processing device will be described, and the weight value changing unit 30a will be described. The back propagation method, which is a learning algorithm to be implemented, which is a learning algorithm for determining the value of the weight value of the internal connection of the known hierarchical network structure, will be described in detail.

In a known hierarchical network structure, a hierarchical network is basically configured from a kind of node called a basic unit and an internal connection having a weight value. FIG. 3 shows the basic configuration of the basic unit 1. This basic unit 1
A multi-processing unit 2 for multiplying a plurality of inputs by respective weights of the internal connection, an accumulating unit 3 for adding all multiplication results thereof, And a threshold processing unit 4 for performing a non-linear threshold processing on the output of the first and second outputs.

Assuming that the h-th layer is the first layer and the i-th layer is the second layer, the accumulation processing unit 3 executes the operation of the following expression (1), and the threshold processing unit 4 executes the operation of the following expression (2).

y _pi = 1 / (1 + exp (−x _pi + θ _i )) (2) where h: unit number of h layer i: unit number of i layer p: pattern number of input signal θ _i : i number of i layer Unit threshold value W _ih : Weight value of internal connection between _{hi and} i layers x _pi : Sum of products of inputs from each unit of h layer to unit i of y layer y _ph : h layer of input signal of p-th pattern Output from the h-th unit y _pi : Output from the i-th unit in the i-th layer with respect to the input signal of the p-th pattern Then, in the known hierarchical network structure section, a number of basic units 1 having such a configuration are used to input signal values. As shown in FIG. 4, an input unit 1 'for directly distributing and outputting an input signal is hierarchically connected as shown in FIG. 4 to form a hierarchical network, and an input pattern (input signal) is converted to a corresponding output pattern (input signal). Output data) It will exert its logical functions.

In the back propagation method, learning is performed by adaptively and automatically adjusting the weight value W _ih and the threshold value θ _i of the hierarchical network by error feedback. (1)
As is apparent from the equation (2), the adjustment of the weight value W _ih and the threshold value θ _i needs to be performed simultaneously, but this operation is a difficult operation that interferes with each other. Therefore, the applicant has
As previously disclosed in "Japanese Patent Application No. 62-333484 (filed on December 28, 1987," Network Configuration Data Processing Apparatus ")," 1 "is always output to the h layer on the input side. In addition, it has been proposed to provide a unit for assigning the threshold value θ _i as a weight value to the output, thereby incorporating the threshold value θ _i into the weight value W _ih and treating the threshold value θ _i as a weight value. By doing so, the above (1) and (2)
ceremony, y _pi = 1 / (1 + exp (−x _pi )) (4)

Next, a description will be given of a weight value learning processing method by the back propagation method according to the description format of the expressions (3) and (4). Here, the description will be made on the assumption that the hierarchical network unit 11 has a hierarchical network having a three-layer structure of the h layer-i layer-j layer shown in FIG.

By analogy with equations (3) and (4), the following equations (5) and (6)
An expression is obtained. That is, y _pj = 1 / (1 + exp (−x _pj )) (6) where j: the unit number of the j-th layer W _ji : the weight of the internal connection between the i-j layers x _pj : the j-th layer from each unit of the i-th layer Y _pj : output from the j-th unit in the j-th layer with respect to the p-th pattern input signal. The weight value changing unit 30a outputs when the input pattern for learning is presented. Output pattern y _pj from output layer
And a teacher pattern d _pj which is a signal to be taken by the output pattern y _pj (j layer j for the p-th pattern input signal)
Receiving the teacher signal to the second unit), first, the difference value [d between the output pattern y _pj and the teacher pattern d _pj [d
_pj− y _pj ], then α _pj = y _pj (1−y _pj ) (d _pj −y _pj ) (7) Here, ε: learning constant ζ: momentum t: number of times of learning According to, the update amount ΔW _ji (t) of the weight value between the i-th layer and the j-th layer
Is calculated. Here, for adding those pertaining to the update amount of the weight values determined in the previous update cycle of "ζΔW _ji (t-1)" is order to speed up the learning.

Subsequently, the weight value changing unit 30a firstly uses the calculated α _pj to , Then , The update amount ΔW _ih (t) of the weight value between the h layer and the i layer
Is calculated.

Subsequently, the weight value changing section 30a, the weight value _{W ji (t) = W ji} (t-1) + ΔW ji (t) W ih (t) = W for the next update cycle according to the update amount calculated as described above By repeating the method of determining _ih (t−1) + ΔW _ih (t), an output pattern y _pj from the output layer that is output when an input pattern for learning is presented, and its output weight value W _ji where the teacher pattern d _pj is the signal to be taken by the pattern y _pj is to be consistent, and treated to learn the W _ih.

Then, the hierarchical network unit 11 includes the g-layer-h-layer-i-layer-
When a four-layer hierarchical network called a j-layer is provided, the weight value changing unit 30a first , Then The update amount ΔW _hg (t) of the weight value between the g layer and the h layer is calculated according to the following formula. It is processed so that it is determined while using it.

Here, when the basic unit 1 does not include the threshold processing unit 4, the above equation (7) becomes α _pj = (d _pj −y _pj ) (7), and the above equation (9) becomes And the above equation (11) becomes Becomes

Next, the hierarchical network unit 11 characteristic of the present invention will be described.

FIG. 5 shows an embodiment of the hierarchical network unit 11. In the figure, 1 'is the input unit shown in FIG. 4, 1 is the basic unit shown in FIG. 4, 1a is the weight value "1" and "-" for the output values of the two basic units 1 located at the preceding stage. Subtractors 1a and 1b each configured by a basic unit 1 having a multiplied value of 1 "as an input and having no threshold processing unit 4 multiply output values of a plurality of basic units 1 located at the preceding stage by a specified weight value. Is a truth value adder composed of a basic unit 1 which does not include a threshold value processing unit 4 and a threshold value processing unit 4, and 1c is a value obtained by multiplying an output value of a truth value adder 1b located at a preceding stage by a specified weight value. This is a center-of-gravity derivation value calculator configured as an input and configured by the basic unit 1 that does not include the threshold processing unit 4.

Reference numeral 40 denotes a prerequisite part truth value calculation layer constituting the hierarchical network unit 11, which receives an input control state quantity and calculates a truth value of a membership function for the control state quantity, and 41 denotes a hierarchical network. A first rule layer constituting the unit 11 for executing a function conversion process assigned to the truth value of the membership function for the control state quantity calculated by the antecedent part truth value calculation layer 40; Calculate the grade value with, 42 is the hierarchical network section
11 is a second rule layer, the first rule layer
One that calculates the grade value by executing the function conversion process assigned to the grade value calculated in 41,
Reference numeral 43 denotes a consequent part truth value processing layer that constitutes the hierarchical network unit 11, and is a function of a membership function for a control operation amount that is reduced or expanded based on the grade value calculated by the second rule layer 42. The calculation of the sum, 44 is a center-of-gravity derived value calculation layer constituting the hierarchical network unit 11, and the consequent part truth value processing layer 43 uses the center-of-gravity derived value used when obtaining the center of gravity value which is a fuzzy inference value. It is calculated from the calculated function sum.

In the embodiment of FIG. 5, the antecedent part truth value calculation layer 40
The input layer (configured by the input unit 12) and the calculation layer (configured by the processing unit 13) described in FIG. 1 are configured. Further, the consequent part truth value processing layer 43 and the center-of-gravity derivation value calculation layer 44 constitute the output layer (configured by the output unit 16) described in FIG.

As shown in FIG. 5, the antecedent part truth value calculation layer 40 has the membership of the input control state quantity such as the truth value of X1 obtained by the membership function SA for the control state quantity X1. Performs the function of calculating the truth value of a function. 6 and 7 show an embodiment for realizing the function of the antecedent truth value calculation layer 40. FIG.

The embodiment shown in FIG. 6 has an output value y output from the basic unit 1. However, as shown in FIG. 6 (a), when ω <0, θ <0, a function shape similar to the membership function “low temperature” shown in FIG. 8 is obtained, and ω> 0 , θ> 0, focusing on the fact that the function shape is similar to the membership function “high temperature” shown in FIG. 8, and as shown in FIG.
By appropriately setting the weight value ω and the threshold value θ to be assigned to the input of, the processing of calculating the truth value of the membership function having the function shape of “low temperature” or “high temperature” in FIG. 8 is realized. Disclose something.

In the embodiment shown in FIG. 7, the difference y between the output values output from the two basic units 1 However, as shown in FIG. 7 (a), focusing on the fact that the function shape is similar to the membership function "temperature is normal" in FIG. 8, and as shown in FIG. 7 (b), It comprises two basic units 1 and the above-described subtractor 1a for processing to calculate the difference between the output values of the two basic units 1, and the weights assigned to the inputs of the two basic units 1 FIG. 8 discloses a method of calculating the truth value of a membership function having a function shape of “normal temperature” by appropriately setting the values ω ₁ and ω ₂ and the threshold values θ ₁ and θ ₂ . I have.

As shown in FIG. 5, the consequent part truth value processing layer 43
A grade value associated with the control function amount Y, MM, or LA for the control operation amount Y is output from the rule layer 42 of, so that the corresponding membership function for the same control operation amount is reduced according to the grade value ( (May be enlarged) and execute the function of calculating the function sum of the reduced membership function. FIG. 9 shows an embodiment for realizing the function of the consequent part truth value processing layer 43. Although the embodiment of FIG. 5 discloses that there is only one type of control operation amount, when there are a plurality of types such as a valve opening degree and a heating amount of a heater, each control operation amount is reduced. A function for calculating the function sum of the membership functions is provided.

The embodiment shown in FIG. 9 is, as shown in FIG.
The membership function for the same control operation amount is finely divided at equal intervals to identify the truth value y _i of each division,
As shown in FIG. 9 (b), the input of the truth adder 1b to the aforementioned number fraction is prepared in the specified truth y _i (second
(To which an output from the basic unit 1 of the rule layer 42 is given) is assigned a weight value “y _i ” to realize the function of the consequent part truth value processing layer 43.

As shown in FIG. 5, the center-of-gravity derived value calculation layer 44 outputs the function sum of the membership function for the reduced control operation amount from the consequent part truth value processing layer 43. The function of calculating _two center-of-gravity derivation values Y ₁ and Y ₂ required for obtaining the center of gravity is executed. FIG. 10 shows an embodiment for realizing the function of the center-of-gravity derived value calculation layer 44.

In general, a fuzzy controller obtains a barycenter of a figure of a function sum of reduced membership functions for the same control operation amount according to the following equation. A method of calculating a control operation amount which is a fuzzy inference value is employed. Therefore, in the embodiment shown in FIG. 10, two above-described center-of-gravity derived value calculators 1c are prepared, and the weight value of the internal connection with the truth value adder 1b of the consequent part truth value processing layer 43 is provided. As for one of the center-of-gravity derived value calculators 1e, they increase at equal intervals, for example, from 0 to 1 in order of increasing from the minimum value of the control operation amount associated with the truth value adder 1b. A weight value is assigned to the other center-of-gravity derived value calculator 1c. For example, from 0 to -1 in the order of decreasing from the maximum value of the control operation amount associated with the truth value adder 1b. A technique is disclosed that realizes a process of calculating _two center-of-gravity derivation values Y ₁ and Y ₂ by assigning weight values that decrease at equal intervals between them. Note that the final barycenter value is configured to be obtained using the output values of the two barycenter derived value calculators 1c in accordance with arithmetic means provided separately from the hierarchical network unit 11.

That is, with this configuration, one center of gravity derived value calculator
1c, for example, if a description will be given of a case truth adder 1b is 6 _{units, Y 1 = 0 · C 1} +0.2 · C 2 +0.4 · C 3 +0.6 · C 4 +0.8 · C ₅ +1 · C ₆ However, C _i outputs the output value of the truth knowledge adder 1b, contrast, other centroid derived value calculator. _{1c, Y 2 = -1 · C 1} -0.8 · Since C ₂ −0.6 · C ₃ −0.4 · C ₄ −0.2 · C ₅ −0 · C ₆ is output, the output values Y ₁ , Y _{1
Using 2} , Calculate Thus, the center of gravity value described in the above equation (13) is obtained.

In the hierarchical network unit 11 thus configured, the basic unit 1 and the subtractor of the truth value calculation layer 40 of the antecedent part
1a between the basic unit 1 of the first rule layer 41 and the first
Between the basic unit 1 of the rule layer 41 and the basic unit 1 of the second rule layer 42, a non-zero weight value is partially assigned, for example, in a format according to the description of the fuzzy control rule. An inner join will be formed. Note that between the input unit 1 ′ and the basic unit 1 in the antecedent part truth value calculation layer 40, the membership function whose truth value is to be calculated has a different control function such as an unpleasant index defined by temperature and humidity. When it is related to the state quantity, a configuration is adopted in which a plurality of states are internally connected.

In the present invention, the weight values that have not been determined among the weight values of the internal connection of the hierarchical network unit 11 configured as described above are calculated according to the weight value changing unit 30a using actual control data obtained from the control target. It will be processed to learn. That is, the operator calculates the truth value of the membership function of the control state quantity described in the fuzzy control rule for the internal connection of each input of the basic unit 1 of the antecedent part truth value calculation layer 40. In addition to setting the weight value and the threshold value, the function of the membership function of the control operation amount described in the fuzzy control rule for the internal connection of each input of the truth value adder 1b of the consequent part truth value processing layer 43. When a weight value for expressing a pattern is set, a weight value assigned to each input of the basic unit 1 of the first rule layer 41 and a weight value assigned to each input of the basic unit 1 of the second rule layer 42 In order to learn, the actual control data group obtained from the control target is registered in the learning signal storage unit 21 of the learning signal presentation device 20, and then the learning signal presentation device
By instructing the layer 20 to present the registered control data group to the hierarchical network unit 11 and the weight value changing unit 30a, the instruction of learning the weight value of the hierarchical network unit 11 is issued.

When receiving the control state quantity data for learning from the learning signal presentation device 20, the input unit 1 'of the hierarchical network unit 11
Distributes the control state quantity data to the corresponding basic unit 1 of the antecedent part truth value calculation layer 40. Upon receiving the control state quantity data, the basic unit 1 and the subtractor 1a of the antecedent part truth value calculation layer 40 calculate the truth value of the membership function for the control state quantity according to the above-described configuration, and The calculated truth value is output to the basic unit 1 of the first rule layer 41 that is internally connected. Upon receiving this truth value, the basic unit 1 of the first rule layer 41 calculates the grade value according to the above-described equation (2), and transfers the grade value to the basic unit 1 internally connected to the second rule layer 42 at the next stage. When the calculated grade value is output and the grade value is received, the basic unit 1 of the second rule layer 42 calculates the grade value according to the above equation (2), and calculates the following consequent truth. The calculated grade value is output to the truth value adder 1b of the value processing layer 43 which is internally coupled.

Then, when receiving the grade value from the basic unit 1 of the second rule layer 42, the truth value adder 1b of the consequent part truth value processing layer 43 sends the membership function for the control operation amount reduced according to the grade value. And outputs the calculated function sum to a center-of-gravity derivation value calculator 1c that is internally coupled to the next-stage center-of-gravity derivation value calculation layer 44. The center-of-gravity derived value calculator 1c of
The above-described two center-of-gravity derived values Y ₁ and Y ₂ are calculated and output, and in response to this output, a calculation unit (not shown) calculates control operation amount data that is a final output, and outputs the weight value change unit 30a and Output to the learning convergence determination unit 23.

When the control operation amount data is received from the hierarchical network unit 11 in this manner, the weight value changing unit 30a calculates α _pj according to the above equation (7), and calculates the center of gravity derived value calculator 1c according to the equation (8). The update amount of the weight value of the internal connection with the value adder 1b is calculated. Since the weight value of the internal connection is to be fixedly set, the process of updating the weight value according to the calculated update amount is not performed. At the start of the learning process, the two above-mentioned two signals used as teacher signals from the control operation amount data for learning are used.
It is required to calculate Y ₁ and Y _2. This process is performed, for example, on a straight line having a specific slope passing through the learning control manipulated variable data value and a left and right end point of the consequent part membership function. This is performed by a method such as determination according to the intersection with.

Subsequently, using the calculated α _pj , the weight value changing unit 30a calculates β _pi according to the above equation (9), and calculates the truth value adder 1b and the second rule layer 42 according to the equation (10). The update amount of the weight value of the internal connection with the basic unit 1 is calculated.
This weight value is subject to learning when tuning the membership function for the control manipulated variable, but need not be subject to learning when a roughly appropriate membership function is required. Therefore, the process of updating the weight value according to the calculated update amount may not be performed.

Subsequently, using the calculated β _pi , the weight value changing unit 30a calculates γ _ph according to the above equation (11), and calculates the basic unit 1 and the first unit of the second rule layer 42 according to the equation (12). The update amount of the weight value of the internal connection with the basic unit 1 of the rule layer 41 is calculated. Since the weight value of the inner connection is to be learned, the weight value changing unit 30a calculates a weight value for the next update cycle according to the calculated update amount and stores it in the weight value management unit 17a. I do.

Subsequently, the weight value changing unit 30a performs a similar process,
An update amount of the weight value of the internal connection between the basic unit 1 of the first rule layer 41 and the basic unit 1 of the antecedent part truth value calculation layer 40 and the subtractor 1a is calculated. Since the weight value of this internal connection is also to be learned, the weight value changing unit 30a
Calculates a weight value for the next update cycle according to the calculated update amount and stores it in the weight value management unit 17a. Then, the weight changing unit 30a sets the weight of the internal connection in the antecedent part truth value calculation layer 40 to be a learning target when a roughly appropriate membership function of the control state quantity is obtained. Since it is not necessary, the updating process of the weight value can be terminated here.

If the learning convergence determining unit 23 of the learning signal presenting device 20 does not determine the learning convergence of the weight value, the learning signal presenting unit 22 presents the same learning control data group again. The value changing unit 30a repeats the same procedure as the above-described processing to obtain the weight value of the internal connection between the basic unit 1 of the second rule layer 42 and the basic unit 1 of the first rule layer 41. And the basic unit 1 of the first rule layer 41, the basic unit 1 of the antecedent truth value calculation layer 40, and the subtractor.
The update processing of the weight value of the inner connection with 1a is continued. Note that the initial values at the start of the learning of the weight values of these internal connections are set, for example, in random numbers.

By this learning process, the basic unit 1 of the second rule layer 42 and the first unit
Weight between the basic unit 1 of the first rule layer 41 and the basic unit 1 of the antecedent part truth value calculation layer 40 and the subtractor 1a. The weight value of the internal connection is obtained, and this weight value indicates that the hierarchical network unit 11 can control the control target accurately according to the fuzzy control rule. From now on, by finally registering the learned weight value in the weight value management unit 17a, the hierarchical network unit 11 can function as a desired fuzzy controller.

The embodiment disclosed in FIG. 5 includes two rule layers, a first rule layer 41 and a second rule layer 42, corresponding to the antecedent operation and the consequent operation of the fuzzy control rule. Although the present invention has been disclosed in the above, it is also possible to provide a rule layer adopting one or more hierarchical network structures between the first rule layer 41 and the second rule layer 42. It is. In this way, since multi-stage inference is made possible, it becomes possible to construct a fuzzy controller that executes more complicated control logic.

In the embodiment shown in FIG. 5, the first rule layer 41 and the second rule layer 42 are used to calculate a grade value in accordance with one basic unit 1 (rule calculation process).
Has been disclosed, but it is also possible to adopt a configuration in which a grade value is obtained according to a method using a partial hierarchical network in the hierarchical network unit 11.
That is, a basic unit group for realizing a rule operation is secured in the hierarchical network unit 11, and a fixed group learned to realize a prescribed rule operation with respect to the weight value of the internal connection of this basic unit group is acquired. The grade value may be obtained by setting a proper value. In this way, a more complicated calculation function can be realized as a rule calculation.

In the embodiment shown in FIG. 5, the arithmetic processing of the basic unit 1 performs the conversion processing of the sigmoid function represented by the expression (2). However, the conversion processing of the linear function and the conversion processing of the step function are disclosed. It is also possible to realize by processing.

As a method of configuring the hierarchical network unit 11 using hardware components, there is a method disclosed in Japanese Patent Application No. 63-216865 filed by the present applicant.
No. (filed on Aug. 31, 1988, "Network Configuration Data Processing Apparatus").

That is, as shown in FIG. 12, the basic unit 1
Multiplication type for multiplying the output from the preceding layer input through the input switch unit 7 and the weight value held by the weight value holding unit 8
A D / A converter 2a, an analog adder 3a that adds the output value of the multiplying D / A converter 2a and the previous accumulated value to calculate a new accumulated value, and an addition result of the analog adder 3a A sample-and-hold circuit 3b for holding, a non-linear function generating circuit 4a for non-linearly converting data held in the sample-and-hold circuit 3b when the accumulation processing is completed, and an analog signal value of the non-linear function generating circuit 4a to be output to a subsequent layer This is realized by including an output holding unit 5 that holds the data, an output switch unit 6 that outputs data held by the output holding unit 5, and a control circuit 9 that controls each of these processing units. Here, regarding the subtractor 1a, the truth value adder 1b, the center-of-gravity derived value calculator 1c, and the average value calculator 1d, the data held by the sample-and-hold circuit 3b is output to the output holding unit without passing through the nonlinear function generating circuit 4a. 5 will be adopted.

The hierarchical network unit 11 is realized by a configuration in which the basic units 1 having this configuration are electrically connected by one common analog bus 70 as shown in FIG. In the figure, reference numeral 71 denotes a weight output circuit for giving a weight value to the weight holding unit 8 of the basic unit 1, 72 denotes an initial signal output circuit corresponding to the input unit 1 ', and 73 denotes a control signal for data transfer. A synchronous control signal line 74 for transmitting a signal to the weight output circuit 71, the initial signal output circuit 72, and the control circuit 9;
Is a main control circuit for transmitting a synchronization control signal.

In the hierarchical network unit 11 having this configuration, the main control circuit
74 selects the basic unit 1 of the preceding layer sequentially in time series, and synchronizes with the selection processing, and outputs the final output held by the output holding unit 5 of the selected basic unit 1 via the analog bus 70. According to the time-division transmission format, processing is performed so as to output to the multiplication type D / A converter 2a of the basic unit 1 in the subsequent stage. Upon receiving this input, the multiplying D / A converter 2a of the subsequent-stage basic unit 1 sequentially selects corresponding weight values, performs multiplication processing of the input values and the weight values, and performs analog-adder 3a and sample-and-hold operations. The accumulation processing unit 3 constituted by the circuit 3b sequentially accumulates the multiplied values. Subsequently, when all the accumulating processes for the basic unit 1 of the preceding layer are 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, and The output holding unit 5 performs processing to hold the final output of this conversion processing result. And the main control circuit
Reference numeral 74 designates a process in which an output pattern corresponding to the input pattern is output by repeating the same processing for the next subsequent layer using the subsequent layer as a new preceding layer.

The hierarchical network unit 11 can be configured by software means without depending on such a configuration by hardware components. With this configuration, the truth value of the membership function for the control state quantity is accurately managed by a table means or the like, and the managed membership function is searched to execute the antecedent part truth value calculation layer 40. Since the process of calculating the truth value of the membership function with respect to the control state quantity, which is a function to perform, can be realized, it is not limited to the membership function having the function shape as described with reference to FIG. Then, as the rule calculation processing of the first rule layer 41 and the second rule layer 42, a more complicated one can be adopted without being restricted by the calculation processing of the basic unit 1.

Although the illustrated embodiment has been described, the present invention is not limited to this. For example, the applicant has previously filed “Japanese Patent Application No. 63-227825 (filed on September 12, 1988,“ Learning processing method of network configuration data processing device ”)”.
The invention has been disclosed in which a weight value learning process can be realized in a shorter time by improving the back propagation method. However, the present invention provides such an improved back propagation method or back propagation method. A learning method of another weight value other than the modulus can also be used. The method of realizing the arithmetic processing of each unit constituting the hierarchical network unit 11 does not constitute the essence of the present invention, and may adopt any configuration method.

〔The invention's effect〕

As described above, according to the present invention, since a fuzzy controller is constructed according to a hierarchical network structure, a fuzzy controller that executes a desired control logic according to mechanical processing can be constructed. become. Thus, the load on the operator for tuning the fuzzy control rules can be greatly reduced.
Then, instead of using the hierarchical network structure of the known hierarchical network configuration data processing apparatus as it is, the hierarchical network structure is structured so as to conform to the fuzzy control rules. Since the fuzzy control rule mapped to the fuzzy control rule becomes visible from the outside, the consistency with the conventional fuzzy controller can be maintained.

[Brief description of the drawings]

FIG. 1 is a diagram showing the principle of the present invention, FIG. 2 is an embodiment of the present invention, FIG. 3 is a diagram showing a basic structure of a basic unit, FIG. 4 is a diagram showing a basic structure of a hierarchical network, and FIG. 6 and 7 show an embodiment of the antecedent part truth value calculation layer, FIG. 8 shows an illustration of the membership function, and FIG. 9 shows an embodiment of the consequent part truth value processing layer. Embodiments, FIG. 10 is an embodiment of a centroid derived value calculation layer, FIG. 11 is an embodiment of a basic unit, and FIG. 12 is an embodiment of a hierarchical network unit. In the figure, 1 is a basic unit, 1 'is an input unit, 2 is a multiplication processing unit, 3 is an accumulation processing unit, 4 is a threshold processing unit, 10 is a hierarchical network configuration fuzzy controller, 11 is a hierarchical network unit, and 12 is a hierarchical network unit. Input unit, 13 is processing unit, 14 and 15
Is a rule unit, 16a is a processing unit, 16b is an output unit, 17 is an internal state value management unit, 20 is a learning signal presentation device,
21 is a learning signal storage unit, 22 is a learning signal presenting unit, 23 is a learning convergence determination unit, 30 is a learning processing device, 40 is an antecedent part truth value calculation layer, 41 is a first rule layer, and 42 is a second rule layer. A rule layer, 43 is a consequent part truth value processing layer, 44 is a center of gravity derived value calculation layer, and 45 is an average value calculation layer.

──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Ryusuke Masuoka 1015 Uedanaka, Nakahara-ku, Kawasaki City, Kanagawa Prefecture Inside Fujitsu Co., Ltd. 72) Inventor Kazuo Asakawa 1015 Uedanaka, Nakahara-ku, Kawasaki City, Kanagawa Prefecture Within Fujitsu Limited (72) Inventor Shigenori Matsuoka 1 Fujimachi, Hino-shi, Tokyo Fuji Facom Control Co., Ltd. (72) Inventor Hiroyuki Okada Tokyo No. 1, Fujimachi, Hino-shi, Tokyo (56) References JP-A-3-182903 (JP, A) JP-A-2-292602 (JP, A) JP-A-2-189635 (JP, A) JP-A-3-260806 (JP, A) WO 89/11684 (WO, A1) Akira Nishiguchi, "Neural Network Automatic adjustment of fuzzy membership function, "Proc. Of the 15th System Symposium, Proc. Of the Society of Instrument and Control Engineers, October 19, 1989, p. 261-266 (58) Field surveyed (Int. Cl. ⁶ , DB name) G05B 13/00-13/02 G06F 9/44 554 G06F 15/18 JICST file (JOIS)

Claims (2)

(57) [Claims] 1. A hierarchical network configuration fuzzy controller for calculating and outputting a control operation amount corresponding to an input control state amount by executing a fuzzy control rule according to a hierarchical network unit (11). A hierarchical network unit (11) receives a multiplied value of a control state quantity and an internal state value assigned to an internal connection for inputting the control state quantity, and calculates a truth value of a membership function of the antecedent part. A first calculation layer composed of a processing unit, a multiplication value of an output value of a corresponding processing unit of the first calculation layer and an internal state value allocated to an internal connection therebetween are input, and a grade value is input. A first rule layer composed of a plurality of rule units to be calculated, an output value of a corresponding rule unit of the first rule layer, and an internal connection therebetween A second rule layer composed of a plurality of rule units for calculating a grade value by using a multiplied value of a partial state value as an input; an output value of a corresponding rule unit of the second rule layer; Calculates the function sum of the consequent membership function whose size is changed by the output value, with the multiplied value by the discrete function value assigned to the join and cut out from the consequent membership function as an input A second calculation layer composed of a plurality of processing units, and an output value of a corresponding processing unit of the second calculation layer, and an internal connection between the second calculation layer to define a predetermined constant value. Multiplied by the numerical value obtained by
An output layer comprising an output unit, which is a set of two units for calculating a derived value of the control operation amount, a fuzzy controller configured as a hierarchical network. 2. The fuzzy controller according to claim 1, wherein an output value of a corresponding rule unit of a preceding rule layer is provided between the first rule layer and the second rule layer. A hierarchical network characterized in that one or a plurality of rule layers each including one or more rule units for calculating a grade value are provided with a multiplied value by an internal state value assigned to an inner connection as an input. Configuration fuzzy controller.