Higher-order Takagi-Sugeno fuzzy model based on kernel mapping
Xiaowei Yang · Control theory & applications · 2011
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.