Affiliations: Department of Automation, Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, China
Note: [] Corresponding author. Rui Ji, Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China. Tel.: +86 21 34204261; Fax: +86 21 34204038; E-mail: jirui@sjtu.edu.cn
Abstract: This paper establishes a connection between Takagi-Sugeno (TS) fuzzy systems and ε-insensitive smooth support vector regression (ε-SSVR), a smooth strategy for solving ε-SVR. In previous ε-SVR-based fuzzy models, the form of membership functions is restricted by the Mercer condition. The ε-SSVR formulation puts no restrictions on the kernel. Therefore, the proposed ε-SSVR-based TS-fuzzy modeling method relaxes the restriction on membership functions. By applying the reduced kernel technique, the number of fuzzy rules is reduced without scarifying the generalization ability. The computational complexity is also reduced by the reduced kernel technique. The performance of our method is illustrated by extensive experiments and comparisons.
