A New Two-Phase Approach to Fuzzy Modeling for Nonlinear Function Approximation*This work was supported by the Korea Science and Engineering Foundation (KOSEF) through the Biometric Engineering Research Center (BERC) at Yonsei University.

Authors:
Wooyong Chung;Euntai Kim
Affiliations:
The author is with the CILAB, School of Electrical and Electronic Engineering, Yonsei University, 134, Shinchon-Dong, Sudaemun-ku, Seoul, 120-749, Korea.,;The author is with the School of Electrical and Electronic Engineering, Yonsei University, 134, Shinchon-Dong, Sudaemun-ku, Seoul, 120-749, Korea. E-mail: etkim@yonsei.ac.kr
Venue:
IEICE - Transactions on Information and Systems
Year:
2006

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Abstract

Nonlinear modeling of complex irregular systems constitutes the essential part of many control and decision-making systems and fuzzy logic is one of the most effective algorithms to build such a nonlinear model. In this paper, a new approach to fuzzy modeling is proposed. The model considered herein is the well-known Sugeno-type fuzzy system. The fuzzy modeling algorithm suggested in this paper is composed of two phases: coarse tuning and fine tuning. In the first phase (coarse tuning), a successive clustering algorithm with the fuzzy validity measure (SCFVM) is proposed to find the number of the fuzzy rules and an initial fuzzy model. In the second phase (fine tuning), a moving genetic algorithm with partial encoding (MGAPE) is developed and used for optimized tuning of membership functions of the fuzzy model. Two computer simulation examples are provided to evaluate the performance of the proposed modeling approach and compare it with other modeling approaches.