JP2000339166A - Method for generating inference rule of fuzzy control - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically generate the inference rule of fuzzy control and a membership function by executing optimum condition search by means of a mathematical relation model, collecting similar data and using grouped data. SOLUTION: A GA(genetic algorithm) is applied to a mathematical relation (mechanical characteristic) model and optimum condition search is executed. When the total number of data collected for analysis reaches a number required for generating a fuzzy control rule, the antecedent, part variable and the consequent part, variable of fuzzy inference are set and obtained/collected data are arranged (S11). Similar data are collected from them and are divided into groups. For generating the inference rule of fuzzy control, similar data are collected and are divided into the groups among analysis data (S12). A membership function in the group is obtained for every variable and the relation in the group is expressed by using the membership function and the variable so as to obtain the inference rule of fuzzy control (S13).

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a technique for automatically creating a fuzzy control inference rule and a membership function used in a fuzzy control system, and can obtain good inference rule creation data. The present invention relates to a fuzzy control inference rule creation method that enables to obtain a fuzzy control inference rule and a membership function using this data.

[0002]

2. Description of the Related Art Fuzzy control technology has been around for a long time to realize automatic control similar to control by a skilled operator, and has been put to practical use in various fields today.

By the way, in performing fuzzy control, it is necessary to determine a fuzzy rule and a membership function suitable for a system to be fuzzy controlled.

[0004] That is, in the fuzzy control, a skilled person collects know-how, such as what to pay attention to, what to target, what to operate, and how much when the system to be controlled is in what state. This know-how is called "I
F-THEN- ", and the size to be reflected is prepared as a membership function which is a function to reflect human linguistic ambiguity. The degree of performing the operation content in the rule is obtained according to the membership function, the likelihood is evaluated, and the operation with the best evaluation result is performed.

[0005] Therefore, how to make fuzzy rules and membership functions suitable for the system to be fuzzy controlled is an important factor that determines the performance of fuzzy control.

In preparing rules for fuzzy control, that is, in preparing fuzzy inference rules and in defining a membership function for determining the degree of reflection of the rules, the method of preparing the rules is as described above. The most common method is based on experience, but there is another method based on measurement and collection data.

That is, fuzzy logic deals with human linguistic ambiguity. Therefore, rules for fuzzy inference using fuzzy logic (fuzzy inference rules)
In general, the knowledge of a skilled person is used for the creation.

However, there are cases where it is difficult for a skilled person to utilize know-how. In that case, use the measurement collection data obtained by operating the control target system, classify the measurable collection data to be used for creating control rules and divide them into groups (clustering), create variable membership functions and fuzzy inference rules (Refer to "Mr. Bancho,""A fuzzy clustering method using an arbitrary fuzzy evaluation function, one fuzzy rule, automatic generation of membership functions," Journal of the Japanese Fuzzy Society, p. 333-343.1992, 4).

[0009]

In the case of fuzzy inference control, it is necessary to prepare fuzzy inference rules and membership functions. The prepared fuzzy inference rules and membership functions greatly affect fuzzy control performance. give.

That is, the performance of the fuzzy inference rule and the membership function is an important key to the performance of the fuzzy control. When the fuzzy inference rule and the membership function are manually created, Since the variance in quality due to the creator's experience and skill is inevitable, and it takes a lot of time to complete, we will automate the creation of these from the viewpoint of cost reduction and shortening of development time. It is desired.

Generally, the easiest way to create fuzzy inference rules and membership functions is to use the knowledge of a skilled person. However, the method of creating a fuzzy inference rule and creating a membership function using the knowledge of a skilled person is based on the premise that there is a skilled person in operation of a fuzzy controlled system.
The best fuzzy control is possible if the knowledge of the expert can be successfully reflected in fuzzy inference rules and membership functions. However, when using the knowledge of experts, the work of collecting necessary information must rely on humans, and the work of converting the collected information into data also depends on humans. Not suitable for automatic creation.

On the other hand, when the knowledge of a skilled person is not available, the control target system is actually operated under various conditions to collect the obtained measurement data, and the collected data is used to control the control rules. Creating and also using the collected data to create a membership function, but this method can be used to automate the data collection itself, and is suitable for automatically creating fuzzy inference rules and membership functions. .

What is important in this method is the quality and quantity of the collected data.

For example, in the case of fuzzy control, no matter how much data with poor control results is collected, a control rule for improving control cannot be created. Also, even if good data is gathered, if the number of data is small, the number of inference rules will be limited,
Also, since the membership function becomes rough, a satisfactory inference rule cannot be created in this case. In other words, it is desirable that the collected data include a large number of data under a wide range of random conditions.

However, even when data is collected by conducting experiments, there are data in an area where setting conditions are difficult and data cannot be collected in practice, and there is a demand for efficient data collection. In reality, there is naturally a limit to the data that can be collected.

In order to create inference rules for fuzzy control, it is necessary to analyze collected data into clusters and divide the collected data into groups, and select a data group that meets the purpose.
The setting of the group selection criteria at this time becomes a bottleneck in automation.

Accordingly, in developing a fuzzy control inference rule automatic generation system for automatically creating a fuzzy inference rule and a membership function necessary to obtain a system capable of intelligent control, a limited number of obtainable fuzzy control inference rules can be obtained. It is possible to automatically create good quality and quantity data for creating fuzzy control rules from measured collected data, and to analyze collected data into clusters to divide them into groups and select a data group that suits the purpose. There is a demand for the development of a method that can automatically create fuzzy control rules and data for creating membership functions that can eliminate the need.

Therefore, the object of the present invention is to
A large number of data of good quality under various conditions necessary to enable the automatic creation of fuzzy control inference rules and membership functions required to obtain an intelligent controllable system Be able to obtain and provide an automatic classification of these data obtained, and also to enable automatic determination of membership functions and to generate fuzzy control inference rules An object of the present invention is to provide a fuzzy control inference rule generation method.

[0019]

In order to achieve the above object, the present invention is configured as follows. In other words, the present invention provides a method for creating inference rules for fuzzy control necessary to obtain a system capable of intelligent control by using fuzzy control based on limited measurement data obtained by a system to be subjected to fuzzy control. It provides a method to create good quality and quantity data for rule making and to use this data to automatically generate inference rules and membership functions. Using a mathematical relationship model that models the mechanical characteristics of the target system,
By performing an optimal condition search for this mathematical relation model by a genetic algorithm, outputting individual data before sufficient convergence, and collecting individual data having a fitness of a predetermined value or more from among them, the fuzzy control is performed. If the data of the controlled system under various conditions required for creating inference rules can be acquired, and if the total number of collected data reaches the number required for creating inference rules for fuzzy control, similar The data is collected and divided into groups. For the membership function, the grouped data is used for each variable of the antecedent variable that is the input variable and the consequent variable that is the variable to be inferred. Calculate the average and standard deviation of the data in the group, divide the range of data distribution in the group into appropriate bandwidths, and create a histogram of the data distribution. Then, by normalizing to obtain the distribution shape of the histogram, this is obtained as a membership function, and this is used to express a fuzzy relationship between the variable in the group and the pair of the membership function of the variable within the group. Create control inference rules.

That is, in order to automatically create inference rules for fuzzy control, it is necessary to collect a large number of random measurement data under a wide range of conditions. However, in practice, it is often necessary to create rules with limited collected data.

According to the present invention, a mathematical relation model is created by modeling the mechanical characteristics of the control target system using a small amount of measured data, and an optimal condition search is performed by using a genetic algorithm using the mathematical relation model. By outputting the individual data before the convergence sufficiently and collecting the individual data having the fitness (FIT) equal to or more than a predetermined value, the control object under various conditions necessary for fuzzy inference rule creation System data can be obtained.

Therefore, in the method of the present invention, a small amount of collected data is utilized to the maximum extent to create a sufficient quantity of high-quality data necessary for preparing an inference rule for fuzzy control and to provide the same for generating an inference rule for fuzzy control. be able to.

Further, according to the present invention, when the total number of data acquired and collected by performing the genetic algorithm calculation reaches a number necessary for preparing inference rules for fuzzy control, similar data is collected and divided into groups. For the membership function, using the grouped data, the average and standard deviation of the data in the group k for each variable of the antecedent variable that is the input variable and the consequent variable that is the variable to be inferred are used. By calculating the value, dividing the range of the data distribution within the group into appropriate bandwidths, creating a histogram of the data distribution, normalizing and obtaining the distribution shape of the histogram, this is obtained as a membership function, Inference Rule of Fuzzy Control Expressing Relationships in a Group by Pairs of Variables in a Group and Membership Functions of the Variables To create.

In the fuzzy control, a variable obtained by fuzzy inference is called a "consequent variable" and an input variable for obtaining it is called a "consequent variable". The consequent variable is a controlled variable of the machine, and the input variable for obtaining the variable is the antecedent variable.

To create a fuzzy control rule, it is necessary to collect data similar to the analysis data and divide them into groups. Cluster processing collects similar data and divides them into groups, but it is necessary to find similar data before grouping. This is called cluster analysis.
The similarity between data to be grouped is analyzed, and based on the analysis result, groups having similarities are classified into the same group.

The membership function is generated as follows using the grouped data. That is, when the membership function Mk for the group k is to be generated, first, the average and the standard deviation of the data in the group k of a certain variable X are calculated, and the range of the data distribution in the group is defined as Divide into appropriate bandwidths and create a histogram of the data distribution. Then, the maximum value of the histogram is set to “1”, and the numerical value of the histogram is corrected (that is, normalized). After the normalization, a straight line passing through the vertices of each histogram or a straight line passing through the maximum value "1" and including the vertices of the left and right histograms is obtained, and the shape of the membership function Mk is obtained. This is used to create a fuzzy control inference rule that expresses a relationship within the group by a pair of a variable and a membership function of the variable in the group.

As a result of this processing, it is possible to automatically perform generation processing from data collection to generation of inference rules for fuzzy control and generation of membership functions.

Therefore, according to the present invention, it is possible to obtain data of sufficient quantity and quality for fuzzy rule creation from a small amount of actually measured data, and it is not necessary to collect data. The process up to inference rule creation and membership creation can be automatically generated in the computer, so fuzzy control inference rules can be automatically updated even when developing a new control target system or new processing conditions. This eliminates the need to create inference rules for fuzzy control, and contributes to reducing development costs, development time, and human resources when developing and improving the control target system.

[0029]

Embodiments of the present invention will be described below in detail with reference to the drawings.

The basic principle of the present invention is that actual measurement data is collected and a model of the mechanical characteristics of the target machine is created by, for example, a neural network. Then, a genetic algorithm (GA) is applied to the mechanical characteristic model. The optimum processing conditions are obtained by applying the data. At this time, among the individual data for which the GA calculation does not sufficiently converge (of a small number of generations), data with a good fitness (FIT) of a certain value or more are collected as analysis data, By collecting the data under different search conditions, a sufficient number of high-quality data for analysis is obtained, and the data for analysis is cluster-analyzed and divided into groups. Generate functions and inference rules. The grouping is performed by dividing the distance value data as a result of the cluster analysis into two, large and small, and grouping the distance data (division distance) that minimizes the sum of the variances. The shape of the membership function is obtained by calculating the average and the standard deviation S of the data for one variable included in a certain group,
区間 3S section is divided appropriately to create a histogram,
Find from that pattern. In addition, the relationship between the input and output of the group is expressed by using each variable in each group obtained in this way and the membership function of each variable, and is set as an inference rule.

In the present invention, the target system of the fuzzy control is operated, the target system of the actual fuzzy control is operated, the measured data is collected, and this is divided into input variables and output (evaluation) variables. An environment capable of generating data under various conditions by a genetic algorithm using the mathematical relation model by creating a mathematical relation model showing a relation between the input variables and the output variables based on its mechanical characteristics. Have gained.

When the optimum condition search is performed by the genetic algorithm using the mathematical relation model, various data similar to those obtained when the fuzzy control target system is operated under various conditions appear before the convergence. . As a result, it is possible to secure an environment in which any number of data items for fuzzy rule creation can be collected.

Therefore, GA (Genetic Algorithm) is applied to the mathematical relation (mechanical property) model to search for the optimal condition, and the GA from the initial state to the point of sufficient convergence is obtained.
By outputting the individual data as a result of the calculation and leaving the input data whose fitness is more than the allowable value, appropriate data selection becomes possible. By changing the search conditions and repeating until the data reaches, sufficient data in both quality and quantity can be collected.

Then, for such a generated data group, the antecedent variables and the consequent variables of the fuzzy inference are set, the data is organized, and rules are created. (In fuzzy control, the variables to be obtained by fuzzy inference (hereinafter referred to as consequent variables) are the control variables of the machine, for example, the number of revolutions, pressure, etc., and the input variables for obtaining them (the Variables are search conditions, processing accuracy, and temperature.) Thus, in the method of the present invention, the amount of data necessary for fuzzy control rule creation is created by making the most of small collected data. Enable.

(Embodiments) Embodiments of the present invention will be described below with reference to the drawings. The present invention uses basic data obtained from a fuzzy controlled system as input variables and output data.
(Evaluation) Divided into variables, and for the target system of the fuzzy control, create a mathematical relationship model showing the relationship between the input variables and output variables based on the mechanical characteristics, and apply a genetic algorithm by changing the conditions to this model By acquiring the data during the convergence by performing the optimal condition search that has been performed, the data under various conditions is collected, and the rules generation of fuzzy inference and membership function generation are performed using these collected data. To do it.

A specific description will be given.

FIGS. 1 and 2 show the data creation procedure of the present invention.
FIG.

FIG. 1 shows a procedure of a method for collecting a sufficient number of high-quality data necessary for generating inference rules and membership functions of fuzzy control, and FIG. 2 shows a method for collecting such data. It shows a procedure for generating inference rules and membership functions for fuzzy control using data. This will be specifically described. First, the data collection procedure will be described.

Step [a]: First, various data obtained by operating the system to be controlled are collected as basic data (S1 in FIG. 1).

The basic data is data measured under different processing conditions, and includes processing condition data such as rotation speed, pressure, and temperature, and processing evaluation data such as processing accuracy. Although various processing conditions are desired, in general, the conditions and ranges that can be collected are not sufficient because of conditions that cannot be set, time constraints, costs, and the like.

Step [b]: Next, the basic data obtained in step [a] is divided into input variables and output (evaluation) variables.
With respect to the target system of the fuzzy control, a mathematical relation model showing the relation between the input variables and the output variables based on the mechanical characteristics is created (S2 in FIG. 1). This mathematical relation model is created as a neural network model.

If this mathematical relation model (neural network model) is used to search for optimal conditions by a genetic algorithm as described later, data under various conditions can be obtained in a simulation manner, and fuzzy rules can be created. It becomes possible to increase the number of data.

Here, the above-described neural network model is a machine characteristic model that inputs machining condition data and outputs machining evaluation data, and creates measurement data as teacher data.

Step [c]: Next, a GA (Genetic Algorithm) is applied to the mathematical relation (machine characteristic) model created in Step [b] to search for an optimal condition.

That is, the optimal processing conditions are determined using GA. In GA, calculation is usually performed using several tens or more individuals. This individual corresponds to the search calculation condition. The quality of this individual is evaluated based on the fitness. Therefore, before the GA calculation converges, individual data with a good fitness is output and data is collected (S4 and S4 in FIG. 1).
5, S6). That is, in the GA calculation, the search condition is obtained by obtaining individual data as an intermediate result of the GA calculation before the optimal condition search sufficiently converges (that is, data in each intermediate generation from the initial generation to the generation before the calculation convergence). It is possible to acquire a large number of data with different search conditions, so if individual data with good fitness is collected among many data obtained by changing search conditions, many data with good quality and different search conditions Can be collected.

In order to collect data of good individuality among the obtained individual data, the individual data obtained in S4 of FIG. 1 is converted into the individuality thereof in S5 of FIG. (S in FIG. 1) by checking whether the value is greater than or equal to an allowable value.
5). As a result, if they match, they are used as fuzzy inference data (data for analysis), and if they do not match, they are discarded (S6 in FIG. 1).

This process (the process of GA calculation, checking of the degree of conformity, and taking in a good one) is repeated with changing the search conditions until the target number of rule conditions is reached (step S3 in FIG. 1).

In this way, a sufficient number of good quality data (data for analysis) can be collected.

Here, the genetic algorithm is a method of probabilistic approximate search proposed based on the mechanism of the evolution or inheritance of an organism, and after generating an initial solution candidate group, adapting each solution candidate. An algorithm that sequentially generates and evaluates a new solution candidate group by evaluating the degree and performing operations such as selection, crossover, and mutation to generate the next solution candidate group, and searches for a solution candidate with higher fitness. It is.

The genetic algorithm is described in detail in, for example, "Iba," Basics of Genetic Algorithm ", Ohmsha, 1994".

The genetic algorithm is an algorithm that sequentially generates a group of solution candidates having a high degree of adaptability by incorporating the mechanisms of evolution and inheritance, evaluates the candidate group, and searches for a solution candidate having a higher degree of fitness. Therefore, there is a high possibility that the best solution can be finally obtained while going back and forth in various situations.

Focusing on this feature, the present invention uses this method so that data on the system to be controlled under various conditions can be obtained by simulation. Attention is paid to the solution candidate data at the halfway point in the genetic algorithm calculation, and the solution candidate data at the halfway point and the data of the control target system at that time by the simulation are collected as analysis data.

That is, in the GA calculation, the input value is set (search condition setting) by using a random number. Therefore, data under various setting conditions is automatically obtained. On the other hand, when the GA calculation proceeds and converges, only certain data can be obtained.

Therefore, GA calculation is performed, and data obtained up to the stage before the calculation converges is collected. That is, data obtained from the initial generation to the generation before the convergence of the calculation is collected. Then, in order to sift out the really necessary data from the collected data, only the data of which the matching degree is equal to or more than the allowable value is left among the collected data. That is, in the genetic algorithm (GA), calculation is usually performed using several tens or more individuals. This individual corresponds to the search calculation condition. The quality of this individual is evaluated based on the fitness. Therefore, by collecting data with good relevance from solution candidate data under various search conditions obtained in each generation (intermediate generation) at a stage where GA calculation does not converge, various data useful for inference rule creation can be obtained. Various data of the system under various conditions can be collected as data for analysis.

FIG. 3 shows an output example of the GA calculation. FIT is a degree of conformity, INPUT corresponds to an input variable and corresponds to a consequent variable, and OUTPUT corresponds to an output variable and corresponds to a consequent variable. Of these, those whose fitness FIT indicates a predetermined value or more are collected.

Step [e]: By repeating the processing of steps [c] and [d] while changing the search conditions until the target number of rule conditions is reached, data under various conditions (for analysis) If the total number of data collected for analysis reaches the number necessary for fuzzy control rule creation as a result of acquiring
Then, the antecedent variable and the consequent variable of the fuzzy inference are set, and the acquired collected data is sorted. Then, this is used to create inference rules for fuzzy control.

In the fuzzy control, a variable obtained by fuzzy inference is called a "consequent variable" and an input variable for obtaining it is called a "consequent variable". The consequent variables are machine control variables,
For example, if the system to be controlled is a machine tool, the rotation speed of the machine tool, pressure, etc., and the antecedent variables, which are input variables for obtaining the same, include search conditions, machining accuracy, temperature, etc. .

A specific example of collection of data (analysis data) for preparing a control rule will be described.

Here, details of the preparation of the mechanical characteristic model as a preparation stage and the GA (genetic algorithm) calculation itself are omitted.

Normally, in the optimal condition search in the GA (genetic algorithm), only the data of the best fit of the GA calculation result is extracted and set as a solution candidate, but the data for preparing the fuzzy inference control rule in the present invention is collected. In doing so,
Of the solution candidates obtained as a result of the GA calculation, all the data of the solution candidates showing the fitness in the range equal to or higher than the designated fitness are targeted instead of the data of the solution candidate showing the best fitness.

That is, in the GA calculation, the input value of the search condition is set by a random number, so that the setting data of various conditions is automatically obtained. On the other hand, when the GA calculation proceeds and converges, only certain data is obtained. Therefore, data at each intermediate stage from the initial stage before convergence to immediately before convergence (or the convergence time) is used.

In this way, the processing of GA calculation, checking of the degree of conformity, and taking in a good one is repeated with changing search conditions until the target number of rule conditions is reached (step S3 in FIG. 1). As a result, a sufficient number of high-quality data (data for analysis) can be collected.

In the fuzzy control, variables obtained by fuzzy inference (consequent variables) are control variables of the machine, for example, rotation speed, pressure, etc., and input variables (consequent variables) for obtaining them are: Search conditions, processing accuracy, temperature.

As described above, the antecedent variables and the consequent variables are read and collected from the GA output data and used as analysis data.

Step [f]: When the collection of the data for analysis is completed, similar data is collected from these and divided into groups. To create inference rules for fuzzy control, it is necessary to collect similar data among analysis data and divide them into groups. Then, a membership function in the group is obtained for each variable, and the relationship within the group is expressed by using the membership function and the variable to form a fuzzy control inference rule.

For example, FIG. 4 is a diagram for explaining the concept of grouping of data groups obtained by the genetic algorithm calculation. The group in a multidimensional space corresponding to the number of variables included in these data is shown in FIG. It is a diagram showing the division,
Referring to FIG. 4, as a result of collecting similar data and dividing them into groups, there are three groups, “group 1”, “group 2”, and “group 3”.
Each group fits by expressing the relationship within the group by a pair of the variables in that group and the membership functions of that variable.
Create two fuzzy control inference rules.

More specifically, if the antecedent variable is Ai, the consequent variable is Bj, and the membership function taken by each variable in the group is Mk, the inference rule is as follows: Can be expressed. Where i, j, k = 1, 2, 3, 4
…….

IF (A1 = M1) (A2 = M2) ... THEN (B1 = M3) (B2 = M4) ... This inference rule states that "if each antecedent variable is a membership function M1. If the antecedent variable A1 and the membership function M2 are applied to the group as an aggregate of the antecedent variable A2 and... To be applied, the consequent variable to be selected is after the membership function M3 is applied. .., Which belongs to the group of the consequent variable B2 to which the membership variable M1 and the membership function M4 are applied. "

As described above, in order to create a fuzzy control rule, it is necessary to collect data similar to analysis data and divide them into groups. Cluster processing collects similar data and divides them into groups. Before grouping,
Work is needed to find similar data. This can be performed by the following method called cluster analysis. That is, assuming that there is data X and data Y to be grouped, the similarity between these data is analyzed.

Then, grouping is performed by using such a cluster analysis result by a method of combining those having similar similarities into the same group.

There are various expression methods for obtaining the similarity between the data X and the data Y, but it is quick to use the Euclidean distance L. The Euclidean distance L can be obtained by the following equation.

L = (Σ (Xik−Yik) ² ) ^1/2 This processing is cluster analysis. This is calculated for each data, and the data having a small distance L is put together to group the data. Here, when a cluster analysis is performed on the data, a tree diagram as shown in FIG. 5 is obtained.

When the cluster analysis is completed, the above-mentioned data is divided into groups. This is a process of collecting data having a small distance L, as described above. More specifically, this process can be performed by dividing the cluster analysis result by a certain distance P. The division distance P is obtained by dividing the number line of the distance L into two at the point P, calculating each variance σ of the divided number line group, and finding P at which the sum of the two variances is minimized. It is obtained by grouping.

By this processing, the data division by the cluster analysis can be automated, and a mechanism for automatically performing the data grouping can be realized (S11 in FIG. 2). In addition,
Each variance σ of the divided number line group is obtained by the following equation.

[0075] Then, a membership function can be obtained by using this variance. Specifically, the membership function is generated as follows. For example, to generate a membership function Mk for a group of k, the following is performed.

First, the data average value and standard deviation value of a certain variable X within the group k, that is, Calculate the standard deviation Sk (= (σk) ^1/2 ), Between, that is, the range of data distribution within the group,
The data is divided into appropriate bands, and a histogram of the data {Xk} (the number of data included in the band) is created. The histogram created in this way is the histogram of FIG.
In FIG. 6, the data is divided into seven by the bandwidth of the standard deviation Sk.

Next, the maximum value of the histogram is set to "1", and the numerical value of the histogram is corrected. That is, normalization is performed. After the normalization, a straight line passing through the vertices of each histogram or a straight line passing through the maximum value "1" and including the vertices of the left and right histograms is obtained, and the shape of the membership function Mk is obtained.

Thus, a membership function Mk for a specific group called k has been generated.

FIG. 7 is an example of a fuzzy inference rule generated and output as described above. The variable part of the antecedent part, the variable part of the consequent part, the membership function part, and the fuzzy inference part are shown.

The structure of the variable part of the antecedent part and the variable part of the consequent part is as follows.
“Variable number”, “variable label name”, “minimum value of variable fluctuation range”, and “maximum value of variable fluctuation range”. Label names and variable ranges are not shown in FIG.
(Genetic algorithm), that is, input data for fuzzy inference rule creation.

In the membership function section, “serial number of membership function”, “number of points indicating shape of polygonal line membership function”, and “X and Y coordinates of each point” are shown. In this rule making software, the number of display points of the membership function Mk is set to three points as shown in a graph surrounded by a dashed line circle indicated by a symbol A in FIG.

Since the inference rules are automatically generated, the label names of the rules are undecided and are not output, similarly to the membership function Mk. The rule part is indicated by a combination of “number of antecedent variable” and “number of membership function” for the antecedent part. The same applies to the configuration of the consequent part.
As with the membership function, the label name of the rule is not output.

FIG. 8 is an output example when fuzzy inference is performed using the fuzzy inference rules as shown in FIG. 7 obtained by automatic generation by the above-described method. “No.1”
In the case of the above, the value "110" is
When the value “1434.08” is given as “consequent part variable 2” and the value “3.23” as “consequent part variable 3”, the value “263.9” is given as “consequent part variable 1”, This indicates that a fuzzy inference result of a value “180.2” as “consequent part variable 2” and a value “45.2” as “consequent part variable 3” is obtained. Are "310", "2
00 ”and“ 0 ”, and the error with respect to the target value is“ −4 ”.
6.1 "," -19.8 ", and" +45.2 ".

In the case of “No. 2”, the value “110” is used as “consequent part variable 1”, the value “917.26” is used as “consequent part variable 2”, and “consequent part variable 3” is used. Value "1"
When "7.59" is given, the value "269.6" is set as "consequent part variable 1" and the value "180.
This indicates that a fuzzy inference result having a value “54.6” has been obtained as “0” and “consequent variable 3”, and the original target values are “310”, “200”, and “100”. And the error with respect to the target value is “-40.4”, “−2”.
0.0 "and" +45.4 ".

In the case of “No. 3”, the value “110” is set as “antecedent variable 1”, the value “3784.52” is set as “antecedent variable 2”, and the value is set as “antecedent variable 3”. The value “8.
55, the value “2” is set as “consequent part variable 1”.
51.2 "and" consequent part variable 2 "as values" 175.
2 "and" consequent part variable 3 "indicate that a fuzzy inference result having a value" 53.4 "was obtained, which means that the original target values were" 310 "," 200 "," 45. 7 ", and the error with respect to the target value is" -58.8 "," -2 ".
4.8 "and" +7.7 ".

Even if it is necessary to make a fine adjustment as a final finish, a shortage is automatically generated based on a small amount of actually measured data, and then an inference rule and a membership function are automatically generated and used. Thus, it can be seen that the inference accuracy is secured such that it is not considered that the fuzzy control is performed.

The details of the present invention have been described above. In short, the present invention employs fuzzy control as a method for collecting data used to create inference rules for fuzzy control necessary for obtaining a system capable of intelligent control. Create good data in both quality and quantity for fuzzy control rule creation from the limited measurement data obtained by the target system and automatically generate inference rules and membership functions using this data. It provides a method that makes it possible to use a mathematical relationship model that models the mechanical characteristics of the controlled system for data collection, and performs an optimal condition search by a genetic algorithm on this mathematical relationship model,
By outputting the individual data before the convergence sufficiently and collecting the individual data having a fitness of a predetermined value or more among them, the control target system under various conditions necessary for creating the inference rules of fuzzy control Data can be obtained.

That is, in order to automatically create an inference rule of fuzzy control, first, it is necessary to collect a lot of random measurement data under a wide range of conditions. However, in practice, it is often necessary to create rules with limited collected data.

In the present invention, a mathematical relationship model is created by modeling the mechanical characteristics of the controlled system using a small amount of measured data, and the optimal condition search is performed by using a genetic algorithm using the mathematical relationship model. By outputting the individual data before the convergence sufficiently and collecting the individual data having a fitness of a predetermined value or more among them, the data of the control target system under various conditions necessary for fuzzy inference rule creation (Data for analysis).

Therefore, in the method of the present invention, a small amount of collected data is utilized to the maximum extent to create a sufficient quantity of high-quality data necessary for preparing a fuzzy control inference rule and to provide the fuzzy control inference rule. be able to.

Further, according to the present invention, when the total number of data acquired and collected by performing the genetic algorithm calculation reaches a number necessary for preparing inference rules for fuzzy control, similar data is collected and divided into groups. For the membership function, using the grouped data, the mean and standard deviation of the data in the group k for each variable of the antecedent variable that is the input variable and the consequent variable that is the variable to be inferred. Is calculated, the range of the data distribution within the group is divided into appropriate bandwidths, and a histogram of the data distribution is created,
By obtaining the distribution shape of the histogram by normalization, this is obtained as a membership function, and this is used for the fuzzy control of expressing the relationship within the group by the pair of the variable in the group and the membership function of the variable. Create inference rules.

In the fuzzy control, a variable determined by fuzzy inference is called a "consequent variable" and an input variable for finding it is called a "consequent variable". The consequent variable is a controlled variable of the machine, and the input variable for obtaining the variable is the antecedent variable.

To create a fuzzy control rule, it is necessary to collect data similar to the analysis data and divide them into groups. Cluster processing collects similar data and divides them into groups. Before grouping, however, it is necessary to find similar data in the data for analysis. This is referred to as cluster analysis, in which a similarity between data to be grouped is analyzed, and a group having similarities is classified into the same group based on the analysis result.

The membership function is generated as follows using the grouped data. That is, when the membership function Mk for the group k is to be generated, first, the average and the standard deviation of the data in the group k of a certain variable X are calculated, and the range of the data distribution in the group is defined as Divide into appropriate bandwidths and create a histogram of the data distribution. Then, the maximum value of the histogram is set to “1”, and the numerical value of the histogram is corrected (that is, normalized). After the normalization, a straight line passing through the vertices of each histogram or a straight line passing through the maximum value "1" and including the vertices of the left and right histograms is obtained, and the shape of the membership function Mk is obtained. This is used to create a fuzzy control inference rule that expresses a relationship within the group by a pair of a variable and a membership function of the variable in the group.

As a result of such processing, it is possible to automatically perform generation processing from data collection to fuzzy control inference rule generation and membership function generation.

The present invention is not limited to the embodiments described above, but can be implemented with various modifications. The method described in the embodiment may be a computer-executable program such as a recording medium such as a magnetic disk (floppy (registered trademark) disk, hard disk, etc.), an optical disk (CD-ROM, DVD, etc.), and a semiconductor memory. Can be stored and distributed.

[0097]

As described in detail above, according to the present invention, data of sufficient quantity and quality for fuzzy rule creation can be obtained from a small amount of actually measured data, and the time and effort of data collection are reduced. In addition, the process from data collection to creation of fuzzy control inference rules and membership function creation can be automatically generated in the computer, so even when developing a new control target system or new processing conditions Automatic updating of inference rules for fuzzy control becomes possible, eliminating the need to create inference rules for fuzzy control, and reducing the development cost, development time, and human resources when developing and improving the system to be controlled. Will be able to contribute to

[Brief description of the drawings]

FIG. 1 is a flowchart showing a processing procedure of a method of the present invention for generating data necessary for creating a fuzzy control inference rule from measurement data.

FIG. 2 is a flowchart showing a processing procedure of a method of the present invention for creating a fuzzy control rule from measurement data.

FIG. 3 is a diagram showing an example of output data obtained by a genetic algorithm calculation.

FIG. 4 is a diagram for explaining the concept of grouping a data group obtained by a genetic algorithm calculation,
FIG. 6 is a diagram showing grouping in a multidimensional space corresponding to several variables included in these data.

FIG. 5 is a diagram illustrating a tree diagram of a cluster analysis result and a method of automatically determining a distance of group division.

FIG. 6 is a diagram for explaining a method of determining a membership function in the method of the present invention.

FIG. 7 is a diagram showing an example of an inference rule of fuzzy control created by the method of the present invention.

FIG. 8 is a diagram showing an example of a result of inference using the fuzzy control inference rules created by the method of the present invention.

Claims (1)

[Claims] 1. A method for automatically generating a membership function and an inference rule used in a fuzzy controlled system, wherein a mathematical relation model that models mechanical characteristics of the controlled system is used for data collection. Search for optimal conditions by genetic algorithm, output individual data before sufficient convergence, and collect individual data with a fitness of more than a predetermined value among them to create inference rules for fuzzy control. Data of the controlled system under various necessary conditions is acquired, and when the total number of collected data reaches a number sufficient to create inference rules for fuzzy control, similar data is collected and divided into groups, and members are collected. For the ship function, the antecedent variables that are the input variables and the variables that are obtained by inference using the grouped data. Calculate the average and standard deviation of the data in the group k for each variable of a certain consequent variable, divide the range of the data distribution in the group into appropriate bandwidths, create a histogram of the data distribution, Fuzzy control inference by expressing the distribution shape of the histogram and obtaining it as a membership function, and using this to express the relationship within the group by the pair of the variable in the group and the membership function of the variable A fuzzy control inference rule creation method characterized by creating a rule.