Design of high performance fuzzy controllers using flexible parameterized membership functions and intelligent genetic algorithms

Shinn-Ying Ho, Tai Kang Chen, Shinn Jang Ho

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

This paper proposes a method for designing high performance fuzzy controllers with a compact rule system. The method is mainly derived from flexible parameterized membership functions (FPMFs) and a novel intelligent genetic algorithm (IGA). Each FPMF consists of flexible trapezoidal fuzzy sets and the fuzzy set is encoded by five parameters. Furthermore, the membership functions and fuzzy rules are simultaneously determined by effectively incorporating all the system parameters into chromosomes. Therefore, the optimal design of fuzzy controllers is formulated as a large parameter optimization problem, which can be effectively solved by IGA. The proposed method is demonstrated by two well-known problems, truck backing and cart centering problems. It is shown empirically that the performance of the proposed method is superior to those of existing methods in terms of the numbers of time steps and fuzzy rules.

Original languageEnglish
Title of host publicationProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
PublisherIEEE Computer Society
Pages1378-1383
Number of pages6
ISBN (Print)0780372824, 9780780372825
DOIs
StatePublished - 1 Jan 2002
Event2002 Congress on Evolutionary Computation, CEC 2002 - Honolulu, HI, United States
Duration: 12 May 200217 May 2002

Publication series

NameProceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
Volume2

Conference

Conference2002 Congress on Evolutionary Computation, CEC 2002
CountryUnited States
CityHonolulu, HI
Period12/05/0217/05/02

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