Local and global estimation of Takagi-Sugeno consequent parameters in genetic fuzzy systems

被引：0
|
作者：
Delgado, MR [1 ]
Von Zuben, F [1 ]
Gomide, F [1 ]
机构：
[1] Univ Estadual Campinas, FEEC, Dept Comp Engn & Ind Automat, BR-13083970 Campinas, SP, Brazil
来源：
JOINT 9TH IFSA WORLD CONGRESS AND 20TH NAFIPS INTERNATIONAL CONFERENCE, PROCEEDINGS, VOLS. 1-5 | 2001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：
In modular and hierarchical evolutionary design of Takagi-Sugeno (T-S) fuzzy systems, an important issue involves the determination of an effective procedure to optimize rule consequent parameters. All the aspects associated with the antecedent part of each fuzzy rule are evolved through generations, and given a specification of the antecedent part of the rules that compose a candidate fuzzy system, the best set of consequent parameters should be determined. This paper investigates the use of global and local least squares optimization procedures to perform this task. Function approximation problems are solved to test the performance of the evolutionary process in comparison with alternative solutions.
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页码：1247 / 1252
页数：6
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