Learning Membership Functions in Takagi-Sugeno Fuzzy Systems by Genetic Algorithms

Abstract

In this paper, we try to automatically induce the membership functions appropriate for the TS fuzzy model. A GA-based learning algorithm is thus proposed to achieve the purpose. The proposed approach considers the shapes of membership functions in fitness evaluation in addition to the accuracy. The shapes of membership functions are evaluated by the overlap and coverage factors, which are used to avoid the bad types of membership functions. The experimental results show that the proposed approach can derive the membership functions in the Takagi-Sugeno system with low errors and good shapes.