International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Building a Fuzzy Model with Transparent Membership Functions through Constrained Evolutionary Optimization

  • Kim, Min-Soeng (Department of Electrical Engineering and Computer Science, KAIST) ;
  • Kim, Chang-Hyun (Department of Electrical Engineering and Computer Science, KAIS) ;
  • Lee, Ju-Jang (Department of Electrical Engineering and Computer Science, KAIST)
  • Published : 2004.09.01
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Abstract

In this paper, a new evolutionary scheme to design a TSK fuzzy model from relevant data is proposed. The identification of the antecedent rule parameters is performed via the evolutionary algorithm with the unique fitness function and the various evolutionary operators, while the identification of the consequent parameters is done using the least square method. The occurrence of the multiple overlapping membership functions, which is a typical feature of unconstrained optimization, is resolved with the help of the proposed fitness function. The proposed algorithm can generate a fuzzy model with transparent membership functions. Through simulations on various problems, the proposed algorithm found a TSK fuzzy model with better accuracy than those found in previous works with transparent partition of input space.

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