Influences of Optimization Criteria on Genetically Generated Fuzzy Knowledge Bases

Marek Balazinski, Sofiane Achiche, Luc Baron

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

Abstract

The need of an expert to build the knowledge base of fuzzy decision support systems is a strong limitation to the expansion of their use in the industry. This paper discusses the influences of the optimization and selection criteria for the automatic generation of fuzzy knowledge bases (FKB) with a genetic algorithm (GA). These criteria allow satisfying two contradictory objectives, i.e., the minimization of the approximation error and the complexity level of FKB. The technique has been successfully applied to the generation of an FKB allowing the prediction of the cutting force from a measure of the feed rate and cutting depth during turning operations. The FKB exhibits a better prediction accuracy than the standard Taylor prediction model.
Original languageEnglish
Title of host publicationInternational Conference on Advanced Manufacturing Technology
Publication date2000
Pages159-164
Publication statusPublished - 2000
Externally publishedYes
EventInfluences of Optimization Criteria on Genetically Generated Fuzzy Knowledge Bases - Johor Bahru, Malaysia
Duration: 16 Aug 200017 Aug 2000
Conference number: 2

Conference

ConferenceInfluences of Optimization Criteria on Genetically Generated Fuzzy Knowledge Bases
Number2
Country/TerritoryMalaysia
CityJohor Bahru
Period16/08/200017/08/2000

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