TSK Fuzzy Systems Based on Fuzzy Partition and Support Vector Machines
CAI Qian-Feng1,2, HAO Zhi-Feng1,2, LIU Wei2
1.College of Computer Science and Engineering, South China University of Technology, Guangzhou 510061 2.Faculty of Applied Mathematics, Guangdong University of Technology, Guangzhou 510090
Abstract An algorithm is presented to design a Takagi-Sugeno-Kang(TSK) fuzzy system with good generalization ability and robustness in high dimensional feature space by fuzzy clustering algorithms and support vector machines (SVM). Firstly, the antecedent membership functions are obtained by fuzzy clustering algorithms in the product space of the input variables. Then, the corresponding consequent parameters of the TSK model can be estimated from data using SVM. The kernel function of the proposed algorithm can be generated by the antecedent membership functions and it is proved to be a Mercer kernel. Finally, experimental results of three well-known datasets show that the proposed method has better generalization ability and robustness than the traditional techniques of TSK model and SVM.
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2009, Vol. 22 
