JPH03271810A - 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 average value 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 average 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 average value of an input signal value inputted to an input layer and to output it to an inference value calculating part 19 n 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 average value of its input signal 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, an arithmetic function for calculating an average value is used as a consequent operation of the fuzzy control rule. The aim is to easily change to the logical OR operation that has been used in the past, and to combine the consequent part calculation means with an input unit that distributes the input signal value and an input from the previous layer. 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 performing a function transformation on the sum of products values, and inputs a plurality of input units as a constituent unit. 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, between the input layer and the foremost intermediate layer, the intermediate layer An internal connection is formed between each other and between the intermediate layer at the final stage and the output layer, and a value is set as the internal state value of the internal connection so that the average 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 amount, in particular, it is possible to realize the arithmetic function of calculating the average value as the consequent operation described in the fuzzy control rule, and to use the logical OR operation that has been used conventionally. The present invention relates to a fuzzy controller that allows for easy modification of the controller.

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 an if-then format, and executes this control algorithm according to fuzzy reasoning to calculate the control operation amount from the detected control state quantity. 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 a control state quantity by executing fuzzy inference according to a fuzzy control rule [Prior art] The fuzzy control rule is as follows: if x+ js big and x2 is
The format is small then y, is big (the IF 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 is the part that describes the conditions for control state quantities such as temperature data). This is the part that describes the conditions for the control operation amount such as At the same time, a configuration is adopted in which the meaning of ambiguous linguistic expressions such as "large" and "small" described in each fuzzy control rule 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. Calculate the membership function value (truth value) of the control state quantity given from 6
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. That is, to explain using the example of the fuzzy control rule mentioned above, if the truth value of "xliSbig" is "0, 8" and the truth value of rx2is smallJ is "0.5", the antecedent part operation Accordingly, processing is performed to determine this "0.5" as the value to be applied to the consequent of the fuzzy control rule.

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 applied to the consequent ')'+ is big, while in another fuzzy control rule, "0.5" is applied to 'y+ is big'. 0.6
” is the applied value, this degree “0.6” is determined as the applied value for the membership function of r big J of the control operation amount y, according to the consequent operation. be.

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 rbigJ 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
In addition to reducing the membership functions such as J according to the determined application value, the control operation amount that will be the output of the fuzzy controller is calculated according to processes such as determining the center of gravity of the figure of the sum of the functions of those 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 controlling 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 the generated fuzzy control rules must be tuned through trial and error while being evaluated through simulations and field tests. By doing so, we have no choice but to complete the fuzzy control rules that are suitable for the control target.

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
A new fuzzy controller that realizes the average value Natsuyama calculation function as the consequent operation described in the fuzzy control rule, and also makes it easy to change to the conventionally used logical sum operation. The purpose is to provide the following.

[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 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 by which the inputs are to be multiplied, 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 and executes 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 constituent units, an input layer composed of a plurality of input units (1''-h), and one layer. or multiple basic units) 1-i
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 1-4, between the basic units 1-4, and between the basic unit 1 and the basic unit 1-j. A hierarchical network structure is adopted in which the basic units lj are interconnected internally. The internal state value management unit 21 is configured to manage internal state values that cause the hierarchical network structure unit 20 to calculate and output the average 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 the average value of the signal values and output it to the inferred value calculation unit 19.

In this way, the consequent calculation unit 1 of the present invention receives the application values of each fuzzy control rule for the same consequent membership function as input signals from the antecedent calculation unit 17. Since the process is to calculate the average value of the input signal values and determine it as the value to be applied to the consequent membership function, after performing the conventional OR operation, This provides a way to achieve more appropriate control in cases where the subject calculation function cannot perform appropriate control.

The data conversion function of the hierarchical network structure unit 20 is characterized in that even if the hierarchical network structure is the same, the content of the 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 1 to n out of a plurality of prepared fuzzy control rules describe "rsmall of control operation amount Y1" as a consequent description, the antecedent part calculation unit 17 As shown in Figure 2, rs of the control operation amount Y1 specified from Rule 1
Application value Z1 for the membership function of “mall”
and rSma of the control operation amount Y defined from Rule 2.
Application value Z for the membership function of l I J
2 and the control operation amount Y defined from rule n, rsm
Application value Z for the membership knob function of allJ,
'Sma 11 J of control operation amount Y1
A process is performed to calculate the applied value of each fuzzy control rule for the membership knob function of . In addition, in Figure 2, for convenience of explanation, these membership function values (truth values)
are shown as being calculated in parallel, but in reality they are calculated one by one in chronological order.

The hierarchical network structure section 20 includes this antecedent section calculation section 17
By performing a consequent operation on the common value given by , processing is performed to determine the applied value for the consequent 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, and the weight values (
A multiplication processing unit 2 that multiplies the internal state values (corresponding to the internal state values explained in FIG. and a dark value processing section 4 which outputs one final output through processing, and assuming that the h layer is the first layer and the i layer is the second layer, the accumulation processing section of the i-th basic unit 1 of the i layer In 3, the following (
1) Executes the calculation of the formula, and the dark value processing unit 4 calculates the following (2)
Process the expression to perform the operation.

X2. =Σy□W,, (1) Formula y
,t! 1/(1+exp(Xpi+θi)) (2
) formula, where h : unit number of h layer p : input signal pattern number θ, : threshold value W of the 1st unit of i layer, h : weight value of internal connection between h-i layer yph : input of P-th pattern Output from the h-th unit of the h layer for the signal The hierarchical network structure section 20 includes the basic unit 1 having such a configuration and the input unit 1' that distributes the applied value calculated by the antecedent section calculation section 17. As a structural unit, as shown in FIG. 4, a plurality of input units i'-h
an input layer composed of one or more basic units 1-i and an intermediate layer provided in one or more stages (in this embodiment, one stage), and one basic unit 1j. and between the input unit 1''-h and the basic unit 1-4, between the basic units 1-i, and between the basic unit 1-i and the basic unit ij. It is configured to have a hierarchical network structure consisting of mutual internal connections.Here, the number of input units l-1'-h is such that the consequent part calculation section 18 is connected to one hierarchical network structure. When implemented in the structure section 20, the fuzzy control rules are prepared in accordance with the number of fuzzy control rules that describe the consequent membership function that will be described most frequently.

The hierarchical network structure section 20 configured in this way is
According to the data conversion function defined by the hierarchical network structure and the weight values assigned to the internal connections of the hierarchical network structure, it exhibits the function of converting an input signal that is input into a corresponding output signal. Therefore, processing is performed to execute the consequent operation according to this data conversion function. The present invention is configured such that the consequent calculation unit 18 calculates and outputs the average value of the input signal values in accordance with the data conversion function of the hierarchical network structure unit 20.

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, 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 holding 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 connected 18 times 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 10 in the subsequent layer sequentially selects the corresponding weight value and multiplies the input value and the weight value, and connects the analog adder 3a and the sample hold circuit. 3b, the accumulation processing unit 3 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 (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 its hierarchical network structure is fixed, its data conversion function can be set according to the weight values assigned to internal connections of the hierarchical network structure. In the present invention, this feature is utilized to configure the consequent part calculation unit 18 to perform the calculation calculation of the average value.

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 constructed 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 outputted from the hierarchical network simulator 30 and the learning teacher signal from the learning signal presentation section 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 determination unit 43 that notifies the learning signal presentation unit 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 layer 7 network configuration data processing device, and in response to presentation of a learning presentation signal by the learning signal presentation bag W40. 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 to create three layers, h-layer-1-j layer, shown in FIG.
To explain it in terms of the layered network structure unit 20, the output signal ypJ from the output layer that is output when a learning input signal (learning presentation signal) is presented, and the signal that the output signal ypJ should take. The learning teacher signal d pJ is
is determined, first, the difference value (dp=7pl) between the output signal yPJ and the learning teacher signal dpj is calculated, and then αpj=)'pj (I VpJ) (dpJy
p=), and then calculate ΔW,, (t)−εΣαpj Y l++ζΔWJ, (
t-1), the update amount ΔW of the weight value between the i layer and the j layer
J.

Calculate (1). Here, L 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.

Subsequently, the learning processing device 50 calculates the calculated α2. First, βp==Ypt(1-yat)ΣαpjW, (t-1
), then ΔWth(t)=εΣβpi )' ph+ζΔWt+
, (t-1), the update amount ΔW3 of the weight value between layer h and layer 1, (1) is calculated. Next, the weight value WJ1(t
) =W,, (t-1)+ΔW, 1(t)Wth(t)
By repeating the method of determining =W, h (t-1) + ΔWr, (t), the output signal ypj from the output layer that is output when the learning presentation signal is presented, and its output Learning teacher signal d which is the signal that signal ypJ should take
The weight values W, . . . , W, ゎ and the threshold θ8. θ, will be learned.

In addition, the present applicant previously applied for the “Japanese Patent Application No. 62-3334”.
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 weighted on the output. It has been proposed that the threshold value θ is incorporated into the weight value W by providing a unit for assigning it as a value, so that the threshold value θ is handled as a weight value. From now on, the threshold value θ will also be learned by the same learning process as the weight value W learning process.

The hierarchical network simulator 30, as shown in FIG.
”, a network structure management unit 31 that manages internal connection relationships of the basic unit 1, an arithmetic control unit 32 that controls the entire arithmetic processing, and each unit of the input unit 1° and the basic unit l of the hierarchical network structure unit 20. An input data expansion unit 33 that temporarily expands input values input to
, 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 1, and output data that manages the arithmetic results of the arithmetic execution unit 35. The management unit 36 is configured to include a 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 30 simulates the data processing function executed by the hierarchical network structure unit 20 implemented 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 average value calculation, the user first generates the input/output signal relationship for the average value calculation and uses the learning signal presentation device l.
I! 40 is registered in the learning signal storage unit 41. That is, a process is performed in which a human output signal relationship is generated in which the input signals and the average value of those input signals are used as an output signal, and is registered in the learning signal storage section 41. FIG. 8 shows the input layer of the hierarchical network structure section 20.
The input/output signal relationship of this average value calculation when the number of Uni7) -h units is two is illustrated. The example in FIG. 8 shows an example in which 81 input/output signal relationships are 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 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 hack 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 section 20 that realize the input/output signal relationship of the registered average value calculation are stored in the weight value management section 34 of the hierarchical network 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 layer 7 notebook work 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 No. 91k(c) is the output signal data for the presentation of each learning presentation signal when the number of learning times is 2596. 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. 8, is given to the hierarchical network simulator 30,
When the number of learning times is 2596, it means that "0.556" is output from the hierarchical network simulator 30.Here, "0.556" is associated with this degree in parentheses. .550'' is the learning teacher signal of [E06J.

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 average value calculation.

FIG. 11 shows the weight values and threshold values (hierarchical network simulator 30
The learning values of (managed by the weight value management unit 34) are illustrated. Here, "0.811879" 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 "1.271083" 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, 1
64371" is the darkness value of the first unit of the middle class, "0.
11) 1082'' represents the darkness 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 average value of the applied values calculated by the antecedent calculation unit 17.

Due to the average value calculation process executed by the consequent part calculation unit 18, there may be cases where appropriate control cannot be performed by the consequent part calculation of the logical sum operation used in the conventional fuzzy controller. 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 OR operation shown in FIG. 12 are registered in the internal state value management unit 21, The network 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 calculation function of the average value calculation as the consequent part calculation 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 has previously filed the patent application No. 1983-227825 (filed on September 12, 1988, network configuration data processing device). The present invention discloses an invention that improves the hack propagation method and makes it possible to realize weight value learning processing in a shorter time. - It is also possible to use another weight value learning method other than the propagation method or the hack propagation method.Also, the hierarchical network structure section 20 itself implemented in the fuzzy controller can be used without using a simulator or the like. Then, the weight value and the dark value may be learned.

〔Effect of the invention〕

As explained above, according to the present invention, it is possible to realize the arithmetic function of calculating the average value as the consequent operation of the fuzzy control rule, while easily changing to the conventionally used disjunction operation. Since it becomes possible to construct a fuzzy controller, it is possible to construct a fuzzy controller that is more suitable for the controlled object, and it can also be easily adapted to cases where it needs to operate as a conventional fuzzy controller. Become.

[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 dark value of the logical sum operation. FIG. 14 is an explanation of the output data when operating as the logical sum calculation function. In the figure, 1 is a basic unit, 1'' is an input unit, 10 is a fuzzy controller, 16 is an antecedent truth value calculation unit, 17 is an antecedent calculation unit, 18 is a consequent calculation unit, and 20 is a hierarchical network. 5 is a structure section, 21 is an internal state value management section, 30 is a hierarchical network simulator, 40 is a learning signal presentation device, and 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 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 as the internal state value to be assigned to the internal connection, and the average value of the input signal values is output from the output layer. fuzzy controller.