T-S fuzzy model identification based on improved interval type-2 fuzzy c-means clustering algorithm

Abstract

In according with nonlinear identification problem, an improved interval type-2 fuzzy c-mean clustering algorithm is proposed. A novel objective function is adapted in improved interval type-2 fuzzy c-mean clustering algorithm, which can reduce the influence of noise on clustering results. The proposed clustering algorithm is applied to T-S fuzzy model premise parameters identification and least squares is used for consequent parameters identification. The proposed identification algorithm is applied to double input single output model and actual thermal power unit main steam temperature data model, the identification results show that, the proposed algorithm has higher identification accuracy.