Influences of Optimization Criteria on Genetically Generated Fuzzy Knowledge Bases

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.