JPH03271809A - Fuzzy controller - Google Patents

Abstract

PURPOSE:To easily change an arithmetic function to an OR operation used from conventionally, while realizing an arithmetic function of an algebraic sum as a consequent part operation by constituting a consequent part arithmetic means of a hierarchical network structure part to which a value by which an algebraic sum value of an input signal value is outputted as an internal state value of an internal coupling from an output layer is set. CONSTITUTION:A hierarchical network structure part 20 operates to calculate an algebraic sum value of an input signal value inputted to an input layer and to output it to an inference value calculating part 19 in accordance with a data converting function prescribed by a hierarchical network structure which said part has by itself and an internal state value allocated to an internal coupling of its hierarchical network structure. In such a way, when an application value of each fuzzy control rule to the same consequent part membership function is inputted as an input signal from an antecedent part arithmetic part 17, a consequent part arithmetic part 18 calculates an algebraic sum value of its input signal value which is inputted and processes it to determine as an application value to its consequent part membership function. Accordingly, the arithmetic contents can be changed freely.

Description

[Detailed Description of the Invention] [Summary] Regarding a fuzzy controller that calculates and outputs a control operation amount corresponding to a control state amount according to a fuzzy control rule, it realizes an algebraic sum calculation function as a consequent operation of the fuzzy control rule. However, with the aim of making it easy to change to the logical sum operation that has been used conventionally, the consequent part calculation means is divided into an input unit that distributes the input signal value, an input from the previous layer, and a corresponding one. A basic unit that receives an internal state value to be multiplied by the input to obtain a sum of products value and obtains an output by converting the sum of products into a function is used as a constituent unit, and multiple input crab units are connected to the input layer. and one or more intermediate layers with one or more basic units as the intermediate layer, and one basic unit as the output layer, and between the input layer and the foremost intermediate layer, and between the intermediate layers. An internal connection is formed between the intermediate layer in the middle and final stage and the output layer, and a value is set as the internal state value of the internal connection, which causes the algebraic sum value of the input signal values to be output from the output layer. It consists of a hierarchical network structure.

Regarding the fuzzy controller that calculates and outputs the seed operation amount, it is particularly important to realize the algebraic sum operation function as the consequent operation described in the fuzzy control rule, while also implementing the conventional logical sum operation. The present invention relates to a fuzzy controller that can be easily modified.

Fuzzy control is becoming popular as a new control processing method. This fuzzy control expresses a control algorithm that includes ambiguity such as human judgment in a 1f-then format, and executes this control algorithm according to fuzzy reasoning to calculate the control operation amount from the detected control state amount. It calculates and controls the controlled object. The fuzzy controller that implements this fuzzy control needs to be configured so that the arithmetic functions described in the control algorithm can be easily changed in order to absorb the ambiguity of the control algorithm.

[Industrial application field]

The present invention provides control corresponding to control state quantities by executing fuzzy inference according to fuzzy control rules [Prior art] The fuzzy control rules are: if X, is big and x, is sm
all then y, is big (I
The F part is called the antecedent part and is a part that describes the conditions for control state quantities such as temperature data, and the THEN part is called the consequent part and describes the conditions for control manipulated variables such as operating terminals. The fuzzy controller manages a plurality of such fuzzy control rules prepared as the control logic of the controlled object, and also writes the control logic in each fuzzy control rule. A configuration will be adopted in which the meaning of ambiguous linguistic expressions such as ``large j'' or ``small'' is quantified and managed as a membership function.

When the fuzzy controller is given control state quantities such as temperature data and water level data from the controlled object, it first calculates the membership function (antecedent membership function) of the control state quantity that it is managing. The membership function value (truth value) of the control state quantity given from is calculated.

Next, in accordance with the antecedent computation of selecting the minimum value, a process is executed to determine the applicable value for the consequent in each fuzzy control rule. In other words, to explain using the example of the fuzzy control rule mentioned above, rx, isbig''
The truth value of is “0.8” and rx, is small
If the truth value of J is "0.5", this "0.5" is determined as the applied value to the consequent of the fuzzy control rule according to the antecedent part calculation. be.

Subsequently, the fuzzy controller follows each fuzzy control rule given for the same membership function (which becomes the consequent membership function) of the same control manipulated variable according to the consequent operation of selecting the maximum value. A process is executed to determine the applied value for the membership function from the applied value for the subject. That is, in the above fuzzy control rule, "0.5" is set as the applied value for ")'+ is big" in the consequent part, and on the other hand, in another fuzzy control rule, "0.5" is set as the applied value for "y+ is big". 0.6
'' is the applied value, processing is performed to determine this "0.6" as the applied value for the membership function of rbtgJ of the control operation amount yl according to the consequent calculation.

Subsequently, the fuzzy controller reduces the membership function of the control manipulated variable according to the determined application value, and
In accordance with the process of finding the center of gravity of the figure of the function sum of this reduced membership function for the same control operation amount, the process of calculating the control operation amount, which is a fuzzy inference value, and outputting it to the operation terminal etc. I will do it. That is, the membership function of rbtgJ of the control operation amount y1 is reduced according to the determined application value, and the rsmal of the control operation amount y1 obtained from another fuzzy control rule is
The control operation amount, which is the output of the fuzzy controller, is calculated by reducing the membership functions such as lJ according to the determined application value, and by calculating the center of gravity of the figure of the sum of the membership functions. It is processed so that

Conventionally, a fuzzy controller having such a configuration includes a consequent calculation function that performs calculation processing to select and output the maximum value of the applied values to be processed, as described above. It was configured like this. The consequent calculation function is implemented by a program means.

[Problem R that the invention attempts to solve]

However, fuzzy control rules are generated according to the knowledge of the target process of an operator who is familiar with controlling the target.

The reality is that it is not possible to generate fuzzy control rules that can achieve the desired control from the beginning, and we will tune them through trial and error while evaluating the generated fuzzy control rules through simulations and on-site tests. Therefore, it is necessary to complete the fuzzy control rules that are suitable for the object to be controlled.

From now on, the consequent part calculation function will also need a different type of calculation function, rather than a logical sum operation that selects and outputs the maximum value of the applied values to be processed. However, conventional fuzzy controllers have a problem in that they cannot meet such demands because they only have the function of calculating the consequent of the logical sum operation. In order to deal with this request, it is possible to prepare the necessary consequent part calculation function as a subroutine in the device in advance, but if such a method is adopted, it will take up a lot of memory capacity, etc. Another problem will arise.

The present invention has been made in view of the above circumstances, and while realizing the function of calculating an algebraic sum as a consequent operation described in a fuzzy control rule, it also provides a function for calculating an algebraic sum as a consequent operation described in a fuzzy control rule. The purpose is to provide a new fuzzy controller that can be easily modified.

[Means to solve the problem] FIG. 1 is a diagram showing the principle configuration 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 control logic for a controlled object,
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 an antecedent membership function management unit that selects fuzzy control rules managed by the fuzzy rule management unit 11 as processing targets; 14 is a consequent membership function management unit which is described in the fuzzy control rule. 15 is an input data reception unit for accepting control state quantity data collected from a controlled object; The unit that executes the processing is an antecedent truth value calculation unit 16, which calculates the truth value of the antecedent membership function of the input control state quantity using the selected fuzzy control rule as a processing unit. 17 is an antecedent calculation unit which performs an antecedent calculation on the truth value of the calculated antecedent membership function to apply the antecedent calculation to the consequent of the fuzzy control rule to be processed. 18 is a consequent part calculation unit which determines the applied value of the consequent part from the applied value for the consequent part of each fuzzy control rule given for the consequent part membership function of the same -. 19 is an inference value calculation unit that determines the applied value for the membership function, and for example, the consequent part members regarding the same control operation amount according to the applied value determined by the consequent part calculation part 18. Control operation amount data, which is a fuzzy inference value, is calculated by reducing the ship function and finding the graphic center of gravity of the function sum of the reduced consequent membership functions.

The consequent calculation unit 18 of the present invention receives one or more inputs and an internal state value to be applied to the East Army with respect to the input, obtains a product sum value, and converts the product sum value into a function. A hierarchical network structure section 20 configured by a hierarchical network structure of a plurality of basic units 1 to obtain an output value by performing a consequent operation, and this hierarchical network structure section 20
and an internal state value management unit 21 that manages internal state values assigned to internal connections of the hierarchical network structure. This hierarchical network structure section 20 has a basic unit 1 and an input unit 1'' that distributes input signal values as structural units, an input layer composed of a plurality of input units 1'-h, and one or more input layers. It has an intermediate layer composed of basic unit) 1-i and provided in one or more stages, and an output layer composed of one basic unit 1-j, and an input unit 1'-h and basic unit) 1. −
A hierarchical network structure is employed in which the basic units 1-i and the basic units 1-i and 1-j are internally connected to each other, and between the basic units 1-i and the basic units 1-j. The internal state value management unit 21 is configured to manage internal state values that cause the hierarchical network structure unit 20 to operate to output an algebraic sum value of input signal values.

(Operation) The hierarchical network structure section 20 of the present invention converts data input to the input layer according to a data conversion function defined by its own hierarchical network structure and internal state values assigned to internal connections of the hierarchical network structure. It operates to calculate the algebraic sum value of input signal values and output it to the inference value calculation unit I9.That is, the input values are Zl, Zz
, ..., z, 1 (0≦2.≦1) is input, calculate and output the algebraic sum value Y defined by setting p = 1.5 in the YagerO formula below. do.

')' = z, V z tV-zll=1△(z
, ”+ z , + ・+ z , IP) l/P In this way, the consequent part calculation unit 18 of the present invention has the same function as the antecedent part calculation unit 17
When the application value of each fuzzy control rule for the same consequent membership function is input as an input signal, the algebraic sum value of the input signal values is calculated and the consequent membership function is calculated. Since the process is to be determined as the applied value to the ship function, it is more suitable for cases where the consequent calculation function that performs the conventional disjunction operation cannot perform appropriate control. This will provide a way to achieve 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 calculation can be changed freely 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 section 21,
It is also possible to immediately return to the same configuration as the conventional fuzzy controller.

〔Example〕

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

FIG. 2 illustrates an embodiment of the present invention. As explained in FIG. 1, 17 in the figure is an antecedent part calculation section, 18 is a consequent part calculation part, 20 is a hierarchical network 7) work structure part, and 21 is an internal state value management part.

The antecedent calculation unit 17 performs processing to calculate the application value of each fuzzy control rule for the same number of consequent memberships. That is, for example, when rules 1 to n out of a plurality of prepared fuzzy control rules describe "rSma 11 of control operation amount Y1" as a consequent description, the antecedent part calculation unit 17 As shown in FIG. 2, the applied value Z1 of the control operation amount YI defined from Rule 1 to the 'Small' membership function, and the rS of the control operation amount Y1 defined from Rule 2.
The application value Z2 for the membership function of ma l I J and r of the control operation amount Y1 defined from the rule n
rSma I of the control operation amount Y1, such as the applied value Z7 for the membership function of Sma I IJ
A process is performed to calculate the application value of each fuzzy control rule to the membership function of IJ. In addition, in FIG. 2, for convenience of explanation, these membership function values (truth values) are shown as being found in parallel, but in reality, they are found one by one in time series.

The hierarchical network structure section 20 includes this antecedent section calculation section 17
By performing the consequent operation on the applied value given by , processing is performed to determine the applied value for the consequent membership function to be processed.

FIG. 3 illustrates the basic configuration of the basic unit l that will constitute the hierarchical network structure section 20. As shown in this figure, the basic unit 1 is a multi-car output system, which multiplies multiple inputs by the weight value of each internal connection (corresponding to the internal state value explained in Fig. 1). A multiplication processing section 2 that adds all the multiplication results, an accumulation processing section 3 that adds up all the multiplication results, and a threshold processing section 4 that performs nonlinear dark value processing on this accumulated value and outputs one final output. Assuming that the h layer is the first layer and the first layer is the second layer, the accumulation processing unit 3 of the i-th basic unit 1 of the i layer has the following (1
), and the threshold processing unit 4 processes to execute the calculation of equation (2) below.

)(,,=ΣyphWih (1
) formula)' pt "' 1 / (1+ eXp(X
pt+θi)) Formula (2) where, h: Unit number of the h layer p: Input signal pattern number θ,: Threshold value of the first unit of the i layer Wlh: h Weight value yph' of the internal connection between the i layers
The output from the h-th unit of the h-layer in response to the input signal of the P-th pattern.The hierarchical network structure section 20 also uses human power to distribute the applied values calculated by the basic unit 1 and the antecedent section calculation section 17 that adopt this configuration. As shown in Fig. 4, a plurality of input crab knits 1''-h
an input layer composed of one or more basic units), an intermediate layer composed of one or more stages (in this example, one stage), and one basic unit) 1
-j, and between the input unit 1-h and the basic unit 1-i, between the basic units 1-4, and between the basic unit 1-i and the basic unit 1-i. -j are configured to have a hierarchical network structure configured by mutually internally connecting them. Here, when the consequent calculation section 18 is implemented in one hierarchical network structure section 20, the number of units in the input unit 1゛-h is the consequent membership function that will be described the most. They will be prepared in accordance with the number of fuzzy control rules to be written.

The hierarchical network structure section 20 configured in this way is
In accordance with the data conversion function defined by the hierarchical network structure and the weight values assigned to the internal connections of the hierarchical network structure, the function is to perform a process of converting an incoming input signal into a corresponding output signal. Therefore, the data converter processes to execute the consequent operation according to the function. In accordance with the data conversion function of the hierarchical network structure section 20, the present invention is configured such that the consequent section calculation section 18 calculates and outputs the algebraic sum value of the input signal values.

The hierarchical network structure section 20 can be constructed by either a program means or a hardware means, but in the case of constructing it by a hardware means, it can be constructed as described in Japanese Patent Application No. 63-216865 filed by the present applicant. 8, 1988
Filed on March 31st, “Network configuration data processing device”)
” can be used.

That is, the basic unit 1, as shown in FIG.
A multiplication type D/A converter 2a that multiplies the output from the previous layer input via the human switch unit 7 and the weight value held by the weight value holding unit 8; and a multiplication type D/A converter 2a.
The analog adder 3a calculates a new cumulative value by adding the output value of and the previous cumulative value, and the sample hold circuit 3b holds the energization result of the analog adder 3a, and the cumulative processing is completed. a nonlinear function generation circuit 4a that nonlinearly converts the data held in the sample and hold circuit 3b when This is realized by including an output switch section 6 that outputs the data held by the section 5, and a control circuit 9 that controls each of these processing sections.

The hierarchical network structure section 20 is realized in a configuration in which basic units 1 having this configuration are electrically connected through one common analog bus 70, as shown in FIG. Here, in the figure, 71 is a weight output circuit that gives a weight value to the weight value holding section 8 of the basic unit 1, 72 is an initial signal output circuit corresponding to the input unit 1, and 73 is a synchronous control signal that is a control signal for data transfer. Signal weighting output circuit 7
1. A synchronous control signal line 74 that transmits the initial signal output circuit 72 and the control circuit 9 is a main control circuit that sends out the synchronous control signal.

In the hierarchical network structure section 20 having this configuration, the main control circuit 74 sequentially selects the basic units 1 in the previous layer in chronological order, and in synchronization with this selection process, holds the output of the selected basic unit 1. The final output held by the unit 5 is transmitted via the analog lot 70 to the multiplication type D/A converter 2 of the basic unit 1 in the subsequent layer according to a time-division transmission format.
Process to output to a. 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 value, multiplies the input value and the weight value, and connects the analog adder 3a and sample hold. The accumulation processing unit 3 constituted by the circuit 3b sequentially accumulates the East Army values. Subsequently, when all the accumulation processing for the basic unit 1 in the previous layer is completed, the main control circuit 74 activates the nonlinear function generation circuit 4a of the basic unit 1 in the subsequent layer to calculate the final output. The output holding unit 5 performs processing to hold the final output of the conversion processing result. Then, the main control circuit 74 sets this latter layer as a new former layer and repeats the same process for the next latter layer, thereby changing the output cover turn (input signal) corresponding to the input cover turn (input signal). It is processed so that it can be output.

The hierarchical network structure section 20 is characterized in that even if the hierarchical network structure is fixed, the data conversion function can be set according to the weight values assigned to the internal connections of the hierarchical network structure. Utilizing this feature, the consequent part calculation unit 18 is configured to perform an algebraic sum calculation.

FIG. 7 illustrates an embodiment of a learning system for learning weight values assigned to internal connections of this hierarchical network structure section 20. Next, according to an embodiment of this learning system, a learning process for determining internal connection weight values of the hierarchical network structure section 20 will be described in detail.

In FIG. 7, 30 is a hierarchical network simulator built on a computer, which simulates the data processing function of the hierarchical network structure section 20, and 40 is a learning signal presentation device, which is a hierarchical network structure section. It manages a group of learning signals (the basic unit is a pair of a learning presentation signal and a learning teacher signal) used for learning the weight values of 20 internal connections, and furthermore, manages a learning presentation signal in the learning signal group. The signal is presented to the hierarchical network simulator 30, and a learning teacher signal is presented to the learning processing device 50, which will be described later. In order to realize this process, the learning signal presentation device 40 reads the learning signals for learning from the learning signal storage section 41 that manages the learning signal group and the learning signal storage section 41, and selects the learning presentation signals among them. a learning signal presentation unit 42 that presents the learning signal to the hierarchical network simulator 30 and presents the other learning teacher signal forming the pair to a learning processing device 50 (described later) and a learning convergence determination unit 43 (described next);
Upon receiving the output signal output from the hierarchical network simulator 30 and the learning teacher signal from the learning signal presentation unit 42, it is determined whether the error of the data processing function of the hierarchical network simulator 30 is within an allowable range. and a learning convergence determining section 43 that notifies the learning signal presenting section 42 of the determination result.

Reference numeral 50 denotes a learning processing device that implements the back propagation method, which is known as a learning algorithm for weight values of the hierarchical network configuration data processing device, and in response to presentation of a 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 weights to be learned are determined based on the error value. By sequentially updating the values, the weight value of the internal connection whose error value falls within the allowable range is learned.

That is, the learning processing device 50 follows the back propagation method and is explained using the hierarchical network structure section 20 having a three-layer structure of layer h - layer 1 and layer j shown in FIG.
Once the output signal ypJ from the output layer, which is output when the human input signal for learning (learning presentation signal) is presented, and the learning teacher signal dp, which is the signal that the output signal ypj should take, first, Then, calculate the difference value Capj-ypi) between the output signal ypJ and the learning teacher signal dllj, then calculate αpj=ypj (1)'pJ)(dpJyp=), and then, ΔWJ1(t)= εΣαpj )' pi+ζΔWJi
According to (t-1), the update amount Δ of the weight value between the i layer and the j layer
Calculate WJ, (1). Here, t represents the number of learning times, and the reason for adding the weight value update amount determined at the previous update cycle is to speed up the learning.

Next, using the calculated α2, the learning processing device 50 first calculates β2□=yp;(1-ypt)Σαp, W, , (t-1
), and then ΔWih(t)=εΣβpiy□+ζΔWth(t
According to D, the update amount ΔWi of the weight value between layer h and layer 1.

Calculate (1). Next, according to the calculated update amount, the weight value for the next update cycle Li(t) =W, (t-1)+ΔWJi(t)W;h(t) =W(
t-1) By repeating the method of determining 1ΔW+h(t), the output signal y2 from the output layer output when the learning presentation signal is presented, and the output signal y
The weight values W j , □, W, h and the threshold value θ that match the learning teacher signal d, , which is the signal to be taken in 2.
8. θ1 will be learned.

In addition, the present applicant previously applied for the “Japanese Patent Application No. 62-3334”.
As disclosed in No. 84 (filed on December 28, 1988, "Network configuration data processing device") J, "1' is always output to the h layer on the input side, and a threshold value θ is weighted on the output. By providing a unit that assigns it as a value, we proposed to incorporate the threshold θ into the weight value W and treat the threshold θ as a weight value.From now on, learning the threshold θ is also similar to learning the weight value W. It will be learned by the learning process.

The hierarchical network simulator 30, as shown in FIG.
a network structure management section 31 that manages internal coupling relationships between the basic units 1 and 1, an arithmetic control section 32 that controls the entire arithmetic processing, and input units 1 and basic units 1 of the hierarchical network structure section 2o. an input data expansion unit 33 that temporarily expands input values input to the , a weight value management unit 34 that manages weight values assigned to each internal connection, and an arithmetic operation unit that is provided to simulate the arithmetic function of the basic unit 1. It is configured to include an execution unit 35 and an output data management unit 36 that manages the calculation results of the calculation execution unit 35. The calculation execution unit 35 includes a multiplication value calculation unit 37 that calculates a multiplication value of the weight value and the input value, and a summation value calculation unit 38 that calculates the summation value of the calculation values of the multiplication value calculation unit 37. It is configured to include a dark value calculation unit 39 that performs threshold processing on the calculated value of the total sum value calculation unit 38.

With this configuration, the hierarchical network simulator 30 simulates the data processing function executed by the hierarchical network structure section 2o installed in the fuzzy controller. Note that the hierarchical network structure section 20
When configuring by program means, it can be realized by using such a hierarchical network simulator 30, for example.

In order to construct the hierarchical network structure section 20 as one that executes an algebraic sum operation, the user first generates an input/output signal relationship for the algebraic sum operation and uses the learning signal presentation device 4.
0 is executed. That is, a process is performed to generate input signals and an input/output signal relationship in which the algebraic sum value of those input signals is used as an output signal, and to register it in the learning signal storage section 41.

Two values z1. The algebraic sum value Y for z z (0≦2.≦1) is expressed as follows: Y=z++Zz Z+ .Zz. From now on, FIG. 8 illustrates the input/output signal relationship of this algebraic sum operation when the number of units per 1 degree input of the human-powered layer of the hierarchical network structure section 20 is two. In this example of Fig. 8, 81
This figure shows an example of generating input/output signal relationships.

Next, the user activates the learning signal presentation device 40 by instructing that the input signal in the registered input/output signal relationship be used as a learning presentation signal and the output signal associated with this as a learning teacher signal. Along with this, we have created a learning processing device that is compatible with this startup process! 50 instructs the weight value management unit 340 of the hierarchical network simulator 30 to start learning as much as possible to realize this input/output signal relationship.

In accordance with this activation instruction, the hierarchical network simulator 30 operates as the hierarchical network structure unit 20 that is implemented (or is scheduled to be implemented) in the fuzzy controller, and converts the presented learning presentation signal into data defined by its weight value. While converting and outputting according to the function,
The learning processing device 50 receives the output signal from the hierarchical network simulator 30, updates the weight value of the inner connection (managed by the weight value management unit 34) according to the bank propagation method described above, and presents the learning signal. The device 40 will perform a process of repeatedly presenting the learning signal until the output signal from the hierarchical network simulator 30 approximately matches the learning teacher signal. Through this process, the weight values of the internal connections of the hierarchical network structure unit 20 that realize the input/output signal relationship of the registered algebraic sum operation are
jA Weight value management unit 3 of the throat work simulator 30
It will be stored in 4.

FIG. 9 shows learning data when learning is performed using the input/output signal relationship shown in FIG. 8 as a learning signal. Here, FIG. 9(a) shows the output signal data of the hierarchical network simulator 30 for presentation of each learning presentation signal when the number of learning times is 0 (that is, at the start of learning), and 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 times is 1000, and FIG. 9(C) is the output signal data for the presentation of each learning presentation signal when the number of learning times is 3407. 3 represents output signal data of the hierarchical network simulator 30. Here, this learning was performed using a hierarchical network structure section 20 whose middle layer consisted of 10 units in one stage. Furthermore, at the start of learning, random numbers generated by a random number generator were assigned as initial values for the weight values (including dark values) of each internal connection. In this figure, for example, when the learning presentation signal of rEO6J, which is the learning signal in FIG. represents what has been done. Here, 0.800, which is associated with this "0.822'' in parentheses, is the learning teacher signal of "E06".

FIG. 1O shows a plot of the number of times of learning and the error of the data processing function of the hierarchical network simulator 30. As shown in this figure, when the number of times of learning exceeds 1000, the hierarchical network simulator 30 outputs a learning teacher signal roughly corresponding to the presentation of the learning signal shown in FIG. In this state, even if something that is not in the learning signal is presented, it will operate to output an output signal that is close to the content of the algebraic sum operation.

FIG. 11 shows the weight values and dark values (hierarchical network simulator 30
The learning values of (managed by the weight value management unit 34) are illustrated. Here, "4.052063" is the weight value of the internal connection between the first unit of the input layer and the first unit of the hidden layer, and "2.188287" is the weight value of the internal connection between the first unit of the input layer and the first unit of the hidden layer. The weight value of the internal connection with the unit, “-2,9
68790" is the threshold value of the first unit of the middle layer, "-1
, 387681'' represents the threshold value of the output layer unit.

As can be seen from Figures 9(c) and 10, this first
If the weight values and darkness values shown in FIG. The calculation unit 18 executes the consequent calculation of calculating and outputting the algebraic sum value of the applied values calculated by the antecedent calculation unit 17.

Due to the execution of the algebraic sum calculation process executed by the consequent part calculation unit 18, there may be cases where appropriate control cannot be executed by the consequent part calculation of the logical sum operation used by conventional fuzzy controllers. This will also provide a way for more appropriate control to be exercised.

On the other hand, when the weight values and dark values shown in FIG. 13 obtained from the learning signal that defines the input-output relationship of the logical sum operation shown in FIG. 12 are registered in the internal state value management unit 21, the hierarchical network The structure section 20 outputs an output signal that provides the logical sum value of the input signals as shown in FIG. 14 in response to the human input of the learning presentation signal shown in FIG. As described above, in the present invention, while realizing the arithmetic function of algebraic sum operation as a consequent operation described in a fuzzy control rule, by simply changing the management data of the internal state value management section 21, the conventional It is also possible to return to the consequent operation of the logical sum operation provided in the fuzzy controller.

Although the illustrated embodiment has been described, the present invention is not limited thereto. For example, in the embodiment, an example of application to the consequent part calculation section 18 has been described, which is powered by two people, but the present invention is not limited to the two-manpower, and can be applied to systems with a large number of inputs. Furthermore, the arithmetic processing of the basic unit 1 is not limited to threshold conversion processing. In addition, the present applicant has previously applied the bank propagation method in the patent application No. 1983-227825 (filed on September 12, 1988, “Learning processing method for network configuration data processing device”). Although the present invention has disclosed an invention that enables learning processing of weight values to be realized in a shorter time by improving the method of A learning method of weight values can also be used. Then, the hierarchical network structure section 2 is implemented in the fuzzy controller without using a simulator or the like.
It is also possible to use 0 itself to learn the weight value and the darkness value.

[Effects of the Invention] As explained above, according to the present invention, it is possible to realize an algebraic sum calculation function as a consequent calculation of a fuzzy control rule, and also easily change to the conventional logical sum calculation. As a result, it becomes possible to construct a fuzzy controller that is more suitable for the controlled object, and it is also easier to use when it needs to operate as a conventional fuzzy controller. It will be possible to respond to

[Brief explanation of drawings]

Figure 1 is a diagram of the principle configuration of the present invention, Figure 2 is an embodiment of the present invention, Figure 3 is a diagram of the basic configuration of the basic unit, Figure 4 is a diagram of the basic configuration of the hierarchical network structure, and Figure 5 is a diagram of the basic configuration of the hierarchical network structure. FIG. 6 is an example of the basic unit; FIG. 6 is an example of the hierarchical network structure; FIG. 7 is an explanatory diagram of the learning system used in the present invention; FIG. 8 is an explanatory diagram of the generated learning signal; and Fig. 10 is an explanatory diagram of learning data, Fig. 11 is an explanatory diagram of learning data of weight values and dark values, Fig. 12 is an explanatory diagram of learning signals generated when assigning logical sum operations, and Fig. 13 is an explanatory diagram of learning data. FIG. 14 is an explanatory diagram of the learning data of the weight value and the dark value of the OR operation. FIG. 14 is an explanatory diagram of the output data when operating as the OR operation function. In the figure, 1 is the basic unit, 1' is the input linear controller), 10 is the fuzzy controller, 16 is the antecedent truth value calculation unit, 17 is the antecedent calculation unit, 18 is the consequent calculation unit, and 20 is the hierarchy. 5 a network structure section; 21 an internal state value management section; 30 a hierarchical network simulator; 40 a learning signal presentation device;
0 is a learning processing device.

Claims (1)

[Claims] An antecedent part truth value calculation unit (16) that calculates a membership function value for the input control state quantity, and a logical operation on the calculated value of the antecedent part truth value calculation unit (16). an antecedent part calculation unit (17) that obtains an output value by applying
), which performs a logical operation on the output value of the consequent part (18) to obtain an output value, and performs a control operation based on the output value of the consequent part calculation part (18) and the membership function value for the control operation amount. In the fuzzy controller, the consequent calculation unit (18) includes an input unit (1') that distributes input signal values, and an inference value calculation unit (19) that calculates and outputs a quantity, and an input unit (1') that distributes an input signal value, and a a basic unit (1) that receives one or more inputs and an internal state value by which the inputs are to be multiplied to obtain a product-sum value, and obtains an output by functionally converting the product-sum value; as a structural unit, a plurality of the input units (1'-h) as an input layer, and one or more of the basic units (1-i) as an intermediate layer, comprising one or more intermediate layers, And one of the basic units (1-j) is used as an output layer, and internal connections are formed between the input layer and the foremost intermediate layer, between the intermediate layers, and between the final intermediate layer and the output layer. and a hierarchical network structure unit (20) in which a value is set to which the algebraic sum value of the input signal values is output from the output layer as the internal state value assigned to the internal connection. Fuzzy controller.