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Acquisition of Fuzzy Control Rules by Neural Technique and Its Applications

神经技术模糊控制规则的获取及其应用

基本信息

批准号：
05680306
负责人：
MIZUMOTO Masaharu
金额：
$ 1.09万
依托单位：
Osaka Electro-Communication University
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1994
项目状态：
已结题

项目摘要

Fuzzy control rules used in fuzzy controls are usually constructed through trial and error by human operators. But it becomes difficult to construct the fuzzy rules by humans when the controlled plants are complicated.This project aims to acquire fuzzy control rules automatically by using neural techniques in tuning the antecedent and consequent parts of fuzzy rules.Fuzzy reasoning method used here is a "fuzzy singleton-type reasoning method" proposed by this investigator. This reasoning method consists of fuzzy control rules whose consequent part is a fuzzy singleton (that is, a real number with a positive weight) , and the method can adjust subtly control results by changing the weights of fuzzy control rules.We give a method of tuning the center and width of the antecedent part as well as the position and weight of the consequent part of fuzzy control rules of fuzzy singleton-type reasoning method by using the deepest decendent method used in the neuro technique. It is shown from the computer simulation of several functions that this method can generate smaller number of fuzzy control rules compared with the case by the simplified reasoning method which is used usually and widely in fuzzy controls.Moreover, we propose a time-variant fuzzy reasoning method which is realized by changing weights with time for fuzzy rules of a fuzzy singleton-type reasoning method. The time-variant fuzzy reasoning method is applied to fuzzy controls in which fuzzy control rules or their groups are changed with time by varying weights of fuzzy control rules, and good control results are obtained.

在模糊控制中使用的模糊控制规则通常是通过人工操作员的尝试和错误来构造的。但当被控对象较复杂时，人工建构模糊控制规则会变得困难，本计画旨在利用类神经技术调整模糊控制规则的前件与后件，以自动取得模糊控制规则，模糊推理方法为研究者提出的“模糊单元组式推理方法”。这种推理方法由模糊控制规则组成，其后件部分是模糊单例（即，具有正权重的真实的数），该方法通过改变模糊控制规则的权值来对控制结果进行微调，给出了一种调整模糊单点控制规则前件中心和宽度、后件位置和权值的方法。类型推理方法，使用神经技术中使用的最深下降方法。对几个函数的计算机仿真表明，与模糊控制中常用的简化推理方法相比，该方法能产生较少的模糊控制规则，并提出了一种时变模糊推理方法，该方法是通过对模糊单值型推理方法中的模糊规则随时间改变权重来实现的。将时变模糊推理方法应用于模糊控制中，通过改变模糊控制规则的权值，使模糊控制规则或其组随时间变化，取得了良好的控制效果。