Predefining Numbers of Fuzzy Sets for Genetically Generated Fuzzy Knowledge Bases Using Clustering Techniques: Application to Tool Wear Monitoring

Sofiane Achiche, Aleksander Stanislaw Przybylo, Luc Baron, Marek Balazinski

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

One of the problems surrounding fuzzy knowledge base generation using genetic algorithms is finding an optimal number of fuzzy sets for each premise. A Genetic algorithm developed by the authors for the automatic generation of fuzzy knowledge bases uses a multi-objective method combining error minimization and simplification. This paper proposes solutions based on cluster analysis and validation indices for the numbers of clusters used in predefining the numbers of fuzzy sets. Two different validation indices as well as a combination of one of these with the multi-objective method are compared to the original multi-objective method on both synthetic and experimental data. Results obtained with the proposed techniques showed a considerable improvement over the multi-objective method on both data sets.
Original languageEnglish
Title of host publicationAnnual Meeting of the North American Fuzzy Information Processing Society (IEEE)
Publication date2006
Publication statusPublished - 2006
Externally publishedYes
Event2006 Annual Meeting of the North American Fuzzy Information Processing Society - Montreal, Canada
Duration: 3 Jun 20066 Jun 2006

Conference

Conference2006 Annual Meeting of the North American Fuzzy Information Processing Society
CountryCanada
CityMontreal
Period03/06/200606/06/2006

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