Evolutionary design of Takagi-Sugeno type fuzzy model for nonlinear system identification and time series

An evolutionary approach for the design of Fuzzy Logic Systems(FLSs) is proposed. Membership functions(MFs) in Takagi-Sugeno type fuzzy logic system is optimized through evolutionary process. Output singleton values are obtained through pseudo-inverse method. The proposed technique is unique for that, to prevent overfilling phenomenon, limited-level RBF membership functions are used and the new fitness function is invented. To show the effectiveness of the proposed method, some simulations results on model identification are given.