Genetic algorithms for the elimination of redundancy and/or rule contribution assessment in fuzzy models

被引：0
|
作者：
Zhao, J
Gorez, R
Wertz, V
机构：
[1] Univ. Catholique de Louvain, Ctr. Syst. Eng. and Appl. Mechanics, Bât. Euler, B-1348 Louvain-La-Neuve, Av. Georges Lemaitre
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 1996年 / 41卷 / 1-2期
关键词：
D O I：
10.1016/0378-4754(95)00066-6
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Takagi-Sugeno fuzzy models may contain redundant rules. The use of genetic algorithms for optimizing a performance index, which combines a modelling error and the number of rules in the model, allows the elimination of redundant rules and a subsequent adjustment of the weights of the rules retained in the model. The method is illustrated by examples.
引用
收藏
页码：139 / 148
页数：10
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