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

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4 Author(s)
S. Achiche ; Department of Mechanical Engineering, École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal, H3C 3A7, Canada, ; M. Balazinski ; A. Przybylo ; L. Baron

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 multiobjective method on both data sets

Published in:

NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society

Date of Conference:

3-6 June 2006