A learning process for fuzzy control rules using genetic algorithms

被引：113
|
作者：
Herrera, F [1 ]
Lozano, M [1 ]
Verdegay, JL [1 ]
机构：
[1] Univ Granada, ETS Ingn Informat, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain
来源：
FUZZY SETS AND SYSTEMS | 1998年 / 100卷 / 1-3期
关键词：
fuzzy logic control systems; learning; genetic algorithms;
D O I：
10.1016/S0165-0114(97)00043-2
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
The purpose of this paper is to present a genetic learning process for learning fuzzy control rules from examples. It is developed in three stages: the first one is a fuzzy rule genetic generating process based on a rule learning iterative approach, the second one combines two kinds of rules, experts rules if there are and the previously generated fuzzy control rules, removing the redundant fuzzy rules, and the third one is a tuning process for adjusting the membership functions of the fuzzy rules. The three components of the learning process are developed formulating suitable genetic algorithms. (C) 1998 Elsevier Science B.V. All rights reserved.
引用
收藏
页码：143 / 158
页数：16
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