Fitting fuzzy membership functions using hybrid particle swarm optimization

被引：10

作者：

Esmin, A. A. A. ^{[1
]}

Lambert-Torres, G. ^{[2
]}

机构：

[1] Univ Fed Ouro Preto, R Diogo de Vasconcelos,122, BR-35400000 Ouro Preto, MG, Brazil

[2] Univ Fed Itajuba, BR-37500503 Itajuba, MG, Brazil

来源：

2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 | 2006年

关键词：

D O I：

10.1109/FUZZY.2006.1681993

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The success of fuzzy application to solve the control problems depends on a number of parameters, such as fuzzy membership functions. One way to improve the performance of the fuzzy reasoning model is made by optimizing the membership functions and the use of evolutionary algorithms. In this paper a Hybrid Particle Swarm Optimization (HPSOM) algorithm is used to optimize the fuzzy membership functions. The HPSOM is able to generate an optimal set of parameters for fuzzy reasoning model based on either, their initial subjective selection, or on a random selection. The purpose of this paper is to present and discuss a different strategy for the membership functions automatic adjustment, using HPSOM algorithm. The proposed approach has been examined and tested with promising results using an application designed to park a vehicle into a garage, beginning from any start position.

引用

页码：2112 / +

页数：3

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