Higher-order Takagi-Sugeno fuzzy model based on kernel mapping
基于核映射的高阶Takagi-Sugeno模糊模型

This paper is concerned with higher-order Takagi-Sugeno(TS) fuzzy systems, where the consequent of a fuzzy rule is a nonlinear combination of input variables. To solve this problem, an implicit nonlinear kernel-mapping is introduced to map the original input space to some higher dimensional feature space, where locally nonlinear submodels of TS fuzzy systems are transformed into locally linear submodels; and then, the expressions of the consequent functions are presented. Furthermore, a novel algorithm of designing higher-order TS fuzzy systems is developed by combining the kernel-based fuzzy clustering with least squares support-vector-machines(LSSVM). Finally, the approximation accuracy, the generalization ability and robustness of the proposed algorithm have been demonstrated by simulation experiments on four well-known data sets.