TS-fuzzy modeling based on epsilon-insensitive smooth support vector regression
This paper establishes a connection between Takagi-Sugeno (TS) fuzzy systems and epsilon-insensitive smooth support vector regression (epsilon-SSVR), a smooth strategy for solving epsilon-SVR. In previous epsilon-SVR-based fuzzy models, the form of membership functions is restricted by the Mercer condition. The epsilon-SSVR formulation puts no restrictions on the kernel. Therefore, the proposed epsilon-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.
fuzzy modeling smooth support vector regression ts-fuzzy systems reference functions epsilon-insensitive learning