Eliciting compact T-S fuzzy models using subtractive clustering and coevolutionary particle swarm optimization

Authors:
Liang Zhao;Yupu Yang;Yong Zeng
Affiliations:
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China;Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China;Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, PR China
Venue:
Neurocomputing
Year:
2009

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Abstract

This paper presents a two-stage method to extract a compact Takagi-Sugeno (T-S) fuzzy model using subtractive clustering and coevolutionary particle swarm optimization (CPSO) from data. On the first stage, the subtractive clustering is utilized to partition the input space and extract a set of fuzzy rules. On the second stage, CPSO algorithm is used to find the optimal membership functions (MFs) and consequent parameters of the rule base. Simulation results on the benchmark modeling problems show that the proposed two-stage method is effective in finding compact and accurate T-S fuzzy models.