Rule pairing methods for crossover in GA for automatic generation of fuzzy control rules

被引：0
|
作者：
Inoue, H [1 ]
Kamei, K [1 ]
Inoue, K [1 ]
机构：
[1] Ritsumeikan Univ, Fac Sci & Engn, Kusatsu, Shiga 5258577, Japan
来源：
1998 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AT THE IEEE WORLD CONGRESS ON COMPUTATIONAL INTELLIGENCE - PROCEEDINGS, VOL 1-2 | 1998年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We had presented fuzzy rule generation methods by Genetic Algorithm. In this paper, we propose three methods to determine rule pairs for crossover in GA for fuzzy rules generation in order to improve search efficiency and reduction of the number of rules. The first two methods are that rule pairs are determined based on a distance between rules of two individuals to be crossed. The third one is that rules of each individual are sorted based on a distance between the origin and a rule center in input space. We apply these methods to fuzzy rules generation for a trailer truck back up control, and we show that the rule sorting method can generate a compact and high performance fuzzy system.
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收藏
页码：1223 / 1228
页数：6
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