A novel self-constructing evolution algorithm for TSK-type fuzzy model design

Sheng-Fuu Lin*, Jyun Wei Chang, Yi Chang Cheng, Yung Chi Hsu

*Corresponding author for this work

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

Abstract

In this paper, a novel self-constructing evolution algorithm (SCEA) for TSK-type fuzzy model (TFM) design is proposed. The proposed SCEA method is different from the traditional genetic algorithms (GA). A chromosome of the population in GA represents a full solution and only one population presents all solutions. Our method applies a population to evaluate a partial solution locally, and several populations to construct the full solution. Thus, a chromosome represents only partial solution. The proposed SCEA uses the self-constructing learning algorithm to construct the TFM automatically that is based on the input data to decide the input partition. And we also adopted the sequence search-based dynamic evolution (SSDE) method to perform parameter learning. Simulation results have shown that the proposed SCEA method obtains better performance than some existing models.

Original languageEnglish
Title of host publication2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
DOIs
StatePublished - 1 Dec 2010
Event2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010 - Barcelona, Spain
Duration: 18 Jul 201023 Jul 2010

Publication series

Name2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010

Conference

Conference2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010
CountrySpain
CityBarcelona
Period18/07/1023/07/10

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