Generation of fuzzy membership function using information theory measures and genetic algorithm

Authors:
Masoud Makrehchi;Otman Basir;Mohamed Kamel
Affiliations:
Pattern Analysis and Machine Intelligence Lab, Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Pattern Analysis and Machine Intelligence Lab, Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, Canada;Pattern Analysis and Machine Intelligence Lab, Department of System Design Engineering, University of Waterloo, Waterloo, Ontario, Canada
Venue:
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Year:
2003

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Abstract

One of the most challenging issues in fuzzy systems design is generating suitable membership functions for fuzzy variables. This paper proposes a paradigm of applying an information theoretic model to generate fuzzy membership functions. After modeling fuzzy membership function by fuzzy partitions, a genetic algorithm based optimization technique is presented to find sub optimal fuzzy partitions. To generate fuzzy membership function based on fuzzy partitions, a heuristic criterion is also defined. Extensive numerical results and evaluation procedure are provided to demonstrate the effectiveness of the proposed paradigm.