Fuzzy Knowledge Base Generation and Optimization Using a Genetic Algorithm

Sofiane Achiche

Research output: Chapter in Book/Report/Conference proceedingBook chapterResearchpeer-review

Abstract

The need of an expert to build the knowledge base of fuzzy decision support systems, herein called the fuzzy knowledge base (FKB), is a strong limitation to the expansion of their use in the industry. This paper presents a genetic algorithm that automatically constructs the FKB and discusses the influences of the optimization and selection criteria on its performances. 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 publicationState of the Art in Fuzzy Logics
Publication date2001
Pages453-467
ISBN (Print)83-88000-64-0
Publication statusPublished - 2001
Externally publishedYes

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