JPH03271808A - 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 the limit 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 a limit 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 a limit 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 a limit 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 manipulated variable corresponding to a control state quantity according to a fuzzy control rule, it realizes a limit sum calculation function as a consequent calculation of the fuzzy control rule. However, with the aim of making it possible to easily 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 an input to obtain a sum of products value and obtains an output by converting the sum of products values into a function is used as a constituent unit, and multiple input 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 and the output layer at the final stage, and a value is set as the internal state value of the internal connection so that the limit sum value of the input signal values is output from the output layer. It consists of a hierarchical network structure section.

Regarding the fuzzy controller that calculates and outputs the control operation amount, in particular, it is possible to realize the marginal sum calculation function as the consequent operation described in the fuzzy control rule, while also implementing the logical sum operation that has been used conventionally. 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 X2 is
5IIlall then y, is big format (The IF part is called the antecedent part, and is the part that describes the conditions for control state quantities such as temperature data, and the TH
The EN part is called the consequent part, and is a part that describes the conditions for the control lI operation amount of the operating end, etc.), and the fuzzy controller describes the control logic of the controlled object. In addition to managing such multiple fuzzy control rules prepared as A management configuration will be adopted.

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. Calculate the membership function value (truth value) of the control state quantity given from 0
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''
If the truth value of is “0.8” and the truth value of rx, is smallJ is “0.5°°,” then according to the antecedent operation, add this “0.5” after the fuzzy control rule. It is processed so that it is determined as a common value for the subject part.

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 applicable value for the membership function from the common value for the subject. That is, in the above fuzzy control rule, "0.5" is applied to the consequent 'V+ is big', and on the other hand, in another fuzzy control rule, ')'+ is big' is set to "0.5". 0.6
” is the applied value, processing is performed to determine this “0.6” as the applied value for the membership function of rbigJ of the control operation amount y1 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 yl is reduced according to the determined application value, and rss+a of the control operation amount y1 obtained from another fuzzy control rule.
The control operation amount that is the output of the fuzzy controller is calculated by reducing the membership functions such as llJ according to the determined application value, and by calculating the center of gravity of the figure of the sum of the functions of these 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.

[Problems to be Solved by the Invention] However, fuzzy control rules are generated according to the knowledge of the target process of an operator who is familiar with the control of the controlled 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 the problem of not being able to meet such demands because they only have the function of performing the consequent part of the logical sum operation. In order to cope with this request, it is possible to prepare the necessary consequent part calculation function as a subroutine in advance in the summer, but if such a method is adopted, the memory capacity will be compressed. Another problem will arise.

The present invention has been made in view of the above circumstances, and while realizing the function of calculating a marginal sum as a consequent operation described in a fuzzy control rule, it also provides a function for calculating a marginal 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 basic 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 Menno ship function, and for example, calculates the consequent for the same control operation amount according to the applied value determined by the consequent calculation unit Control operation amount data, which is a fuzzy inference value, is calculated by reducing the part membership function and finding the graphic center of gravity of the function sum of the reduced consequent part membership function.

The consequent calculation unit 18 of the present invention receives one or more human forces and an internal state value to be multiplied by the input to obtain 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 l-1 to obtain an output value and executes a consequent operation, and this hierarchical network structure section 2
It is comprised of an internal state B value management unit 21 that manages internal state values assigned to internal connections of the 0 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 constituent units, an input layer composed of a plurality of input channels 1-1'-h, and one or more input layers. Multiple basic units 1-4
an intermediate layer provided in one or more stages, and
an output layer constituted by one basic unit 1-j, and between the input unit 1-h and the basic unit Li, between the basic units 1-4, and between the basic unit l-i and the basic unit Li. A hierarchical network structure is adopted in which units 1-j are mutually interconnected. 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 a limit sum value of input signal values.

[Effect]

The hierarchical network structure unit 20 of the present invention processes input 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 a limit sum value of signal values and output it to the inferred value calculation unit 19. That is, as an input value Zl+ZZ
When ff...IZll (0≦2.≦1) is input manually, a limit sum value Y defined by setting P=1 in the following Yager's formula is calculated and output.

Y=z, z2...2+1 -1△(Z l' + Z z' + ...10Z,
,') ``'In this way, the consequent part calculation unit 18 of the present invention receives the applied values of each fuzzy control rule for the same consequent part membership function from the antecedent part calculation part 17 as an input signal. When an input signal is input, it is processed so that the marginal sum value of the input signal values is calculated and determined as the value to be applied to the consequent membership function. This provides a way to realize more appropriate control in cases where appropriate control B cannot be executed by the consequent part calculation function that performs the calculation.

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 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 consequent membership function. That is, for example, when rules l to n out of a plurality of prepared fuzzy control rules describe rSma l IJ of the control operation amount Y1 as a description of the consequent part, the antecedent part calculation unit 17 performs the following: As shown in FIG. 2, the applied value Z for the 'Small' membership function of the control operation amount Y defined from Rule 1, and rS of the control operation amount Y1 defined from Rule 2.
The common value Z2 for the membership function of "ma l l" and r of the control operation amount Y1 defined from the rule n
Application value Z for membership function of smallJ
rSma I l of the control operation amount Y, such as n
'' is calculated for the membership function of each fuzzy control rule. 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 common value for the consequent membership function to be processed.

FIG. 3 illustrates the basic configuration of the basic unit 1 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 dark value processing section 4 that performs nonlinear dark value processing on this accumulated value and outputs one final output. If the h layer is the first layer and the i layer is the second layer, then the accumulation processing section 3 of the i-th basic unit 1 of the i layer processes the following (1
) is executed, and the dark value processing section 4 processes to execute the following (2) second operation.

Xpi-ΣyW , , (1
) formula)'pt=17(1+exp(-Xpi+θi))
(2) where, h : unit number of h layer p : input signal pattern number θ, = dark value W of the 1st unit of i layer (, : weight value of internal connection between h - i layer yph : Pth The hierarchical network structure section 20 receives an output from the hth unit of the h layer for the input signal of the pattern, and an input that distributes the application value calculated by the basic unit 7 1 and the antecedent part calculation section 17 that adopt this configuration. With unit 1 as a constituent unit,
As shown in FIG.
an intermediate layer (one stage in this embodiment) configured by input units l-i and provided in one or more stages (one stage in this embodiment); and an output layer configured by one basic unit 1-j, and an input unit 1°- A hierarchical network structure configured by mutually internally connecting between the basic unit h and the basic unit l-i, between the basic units 1-4, and between the basic unit I-4 and the basic unit 1-j. Constructed to have .

Here, the number of units of the input unit 1''-h is the number of consequent memberships that will be described the most when the consequent calculation unit 18 is implemented in one hierarchical network structure unit 20. The rules will be prepared in accordance with the number of fuzzy control rules that describe the .

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, processing is performed to execute the consequent operation according to this data conversion 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 limit sum value of the input signal values.

The hierarchical network structure unit 20 can be constructed by either a program means or a hardware means, but if it is constructed by a hardware means, it can be constructed as described in Japanese Patent Application No. 1983-216865 filed by the present applicant. 8, 1988
Filed on March 31st, “Network configuration data processing device”)
” can be used.

That is, as shown in FIG. 5, the basic unit 1 is a multiplication type D that multiplies the output from the previous layer input via the input switch section 7 by the weight value held by the weight value holding section 8.
/A converter 2a, an analog adder 3a that adds the output value of the multiplication type D/A converter 2a and the previous accumulated value to calculate a new accumulated value, and holds the addition result of the analog adder 3a. A sample-and-hold circuit 3b that converts the data held in the sample-and-hold circuit 3b into nonlinear form when the accumulation process is completed, and an analog signal of the nonlinear function generation circuit 4a that outputs the data to the subsequent layer. This is realized by including an output holding section 5 that holds a value, an output switch section 6 that outputs the data held by the output holding 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 the 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 synchronization signal that is a control signal for data transfer. A synchronous control signal line 74 that transmits a control signal to the weight output circuit 71, the initial signal output circuit 72, and the control circuit 9 is a main control circuit that sends out a 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, the output holding section of the selected basic unit 1. 5 is transmitted via the analog bus 70 in a time-division manner to the multiplication type D/A converter 2 of the basic unit 1 in the subsequent layer.
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 section 3 constituted by the circuit 3b sequentially accumulates the multiplied 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 (output 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 value assigned to the internal connections of the hierarchical network structure9. Utilizing this feature, the consequent part calculation unit 18 is configured to execute the marginal 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 structured as a hierarchical network structure. The learning signal group (a pair of learning presentation signal and learning teacher signal is used as a basic unit) used for learning the weight value of the internal connection of the unit 20 is managed, and the learning presentation signal within the learning signal group is managed. 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 outputted from the hierarchical network simulator 30 and the learning teacher signal from the learning signal presentation unit 42, the hierarchical network 7) determines whether the error of the data processing function of the work simulator 30 is within an allowable range. This includes a learning convergence determining section 43 that makes a determination and notifies the learning signal presenting section 42 of the determination result.

Reference numeral 50 denotes a learning processing device that implements a back propagation method known as a learning algorithm for weight values of a hierarchical network configuration data processing device, and outputs the learning presentation signal 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 weight value to be studied is calculated based on the error value. By sequentially updating , the weight value of the internal connection that makes the error value fall within the allowable range is learned.

That is, the learning processing device 50 follows the bank propagation method and is explained using the hierarchical network structure unit 20 having a three-layer structure of h layer - 1 layer j layer shown in FIG.
Once the output signal yPJ from the output layer, which is output when a learning input signal (learning presentation signal) is presented, and the learning teacher signal dpj, which is the signal that the output signal ypi should take, are first determined. , the difference value Cdp; )'p=) between the output signal ypJ and the learning teacher signal d0 is calculated, and then αpj=ypj N yp7)(dpi yp;
), and then ΔWji(t)−εΣαIJ3′ Ili+ζΔW,,
According to (t-1), the update amount Δ of the weight value between the i layer and the j layer
Calculate W j 1(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, the learning processing device 50 uses the calculated α2J to first calculate β, □−ypi(1yp;)Σα, JWJi(t−1)
Then, ΔWth(t)−εΣβ2, y□tenζΔwrh(t
t), update amount ΔW of weight values between layer h and layer 1.

Calculate (1). Subsequently, the weight value WJ for the next update cycle is determined according to the calculated update amount, (t) = W
Ji(t-1)+ΔW, , Q)Wth(t)=W
, h(t-1)+ΔWik(t), the output signal y□ from the output layer that is output when the learning presentation signal is presented, and its output signal ypj The weight value W j i + W i k with which the learning teacher signal dpj, which is the signal to be taken, matches the learning teacher signal dpj
and threshold value θ1. θ1 will be learned.

The present applicant previously filed [Patent Application No. 62-3334]
As disclosed in No. 84 (filed on December 28, 1988, "Network configuration data processing device"), 1" is always output to the h layer on the input side, and a threshold value θ is set as a weight value for the output. We proposed that the threshold θ be incorporated into the weight value W by providing a unit that allocates the threshold θ as a weight value.
is also learned by the same learning process as the learning of the weight value W.

The hierarchical network simulator 30, as shown in FIG.
A network structure management section 31 that manages the internal connection relationship of the basic unit 1 and the basic unit 1, an arithmetic control section 32 that controls the entire arithmetic processing, and each unit of the input unit 1'' of the hierarchical network structure section 2o and the basic unit 1. an input data development unit 33 that temporarily develops human power values input to the input data development unit 33;
, a weight value management unit 34 that manages weight values assigned to each internal connection, an arithmetic execution unit 35 that is provided to simulate the arithmetic function of the basic unit 7) 1, and a calculation result of the arithmetic execution unit 35 that is managed. The output data management unit 36 is configured to include an output data management unit 36. 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 calculating section 39 that performs dark value processing on the calculated value of the total sum value calculating section 38.

With this configuration, the hierarchical network simulator 3o 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 unit 20 as one that executes a marginal sum operation, the user first generates an input/output signal relationship for the marginal sum operation and uses the learning signal presentation device 4.
0 is executed. That is, a process is performed to generate an input signal and an input/output signal relationship whose output signal is the marginal sum value of those input signals, and to register it in the learning signal storage section 41.

The marginal sum value Y for two values z I + Z 2 (0≦2.≦1) is expressed as follows: Y=1△(zi+zz). FIG. 8 illustrates the input/output signal relationship of this marginal sum operation in the case where the number of input units 1'-h in the input layer of the hierarchical network structure section 20 is two. In this example of Fig. 8, 8
This figure shows an example in which one input/output signal relationship is generated.

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. At the same time, by activating the learning processing device 50 in response to this activation process, the weight values of the weight value management unit 34 of the hierarchical network simulator 30 enter learning as much as possible to realize this input/output signal relationship. Instruct.

In accordance with this startup instruction, the hierarchical network simulator 30 operates as the hierarchical network structure unit 20 that is implemented (or is planned to be implemented) in the fuzzy controller.
In addition to converting and outputting the presented learning presentation signal according to the data conversion function defined by the weight value, the learning processing device 50 receives the output signal from the hierarchical network simulator 30 and performs the above-mentioned back-up processing. The learning signal presentation device 40 updates the weight value (managed by the weight value management unit 34) of the internal connection according to the propagation method.
In this case, the learning signal is repeatedly presented 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 marginal sum calculation are stored in the weight value management unit 34 of the hierarchical work simulator 30.

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 3867. 3 represents output signal data of the hierarchical network simulator 30. Here, this learning was performed using a hierarchical network structure section 2o in which the 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 rEO6, which is the learning signal in FIG. Indicates that it has been output. Here, "1.000" associated with this "0.976" in parentheses is the learning teacher signal of "E06".

FIG. 10 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 marginal 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, "26.331636" 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 "8.737288" 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, ” −0
, 603080" is the threshold of the first unit of the middle layer,"
-14,004505'' represents the darkness value of the unit in the output layer.

As can be seen from Figures 9(c) and 10, this first
The weight values and darkness values shown in FIG. 1 are used to calculate the internal state B (Ii!
When registered in the management unit 21, the consequent part calculation unit 18, which executes calculation processing by the hierarchical network structure part 20, calculates and outputs the marginal sum value of the applied values calculated by the antecedent part calculation unit 17. The consequent operation will be executed.

Due to the execution of the marginal 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 input of the learning presentation signal shown in FIG. 12. As described above, in the present invention, while realizing the arithmetic function of the marginal sum operation as a consequent operation described in the 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 unit 18 is explained with the help of two people. The arithmetic processing of l is not limited to the dark value conversion processing.In addition, the present applicant previously applied for the "Network configuration data The present invention discloses an invention that improves the bank propagation method and makes it possible to realize weight value learning processing in a shorter time. It is also possible to use other weight value learning methods other than the back propagation method and hack propagation method.And, the N7 node work structure can be implemented in the fuzzy controller without using a simulator or the like. It is also possible to use the section 20 itself to learn the weight values and the dark values.

[Effects of the Invention] As explained above, according to the present invention, it is possible to realize the marginal sum calculation function as the consequent part calculation of the fuzzy control rule, while also easily changing 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 7), 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. Figure 6 is an example of the basic unit; Figure 6 is an example of the hierarchical network structure; Figure 7 is an explanatory diagram of the learning system used in the present invention; Figure 8 is an explanatory diagram of the learning signal to be generated; Figures 9 and 10 are explanatory diagrams of learning data, Figure 11 is an explanatory diagram of learning data of weight values and dark values, Figure 12 is an explanatory diagram of learning signals generated when assigning logical sum operations, and Figure 13 is an explanatory diagram of learning data. The figure is an explanatory diagram of the learning data of the weight value and the dark value of the OR operation, and FIG. 14 is an explanatory diagram of the output data when operating as the OR operation function. In the figure, engineering is a basic unit, lo is an input unit, 10 is a fuzzy controller, 16 is an antecedent part truth value calculation unit, 17 is an antecedent part calculation unit, 18 is a consequent part calculation unit, and 20 is a layer 7 software 1 network structure section, 21 is an internal state value management section, 30 is a hierarchical network simulator, 40 is a learning signal presentation device, 5
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 intermediate layer at the forefront, between the intermediate layers, and between the intermediate cylinder layer at the final stage and the output layer. and a hierarchical network structure unit (20) in which a value is set that will cause a limit sum value of input signal values to be output from the output layer as an internal state value assigned to the internal connection. Features fuzzy controller.