A novel identification method for Takagi-Sugeno fuzzy model

Based on the Xie-Beni index and an improved particle swarm optimization algorithm, a novel identification method for the Takagi-Sugeno fuzzy model is proposed in this paper. Firstly, Xie-Beni indices with a fuzzy c-means clustering algorithm are adopted to find the rule number of the Takagi-Sugeno fuzzy model. By utilizing the particle swarm optimization algorithm, the initial membership function and the consequent parameters of the fuzzy model are obtained. In addition, through an improved fuzzy c-regression model and orthogonal least-square method, the premise structure and consequent parameters can be obtained to establish the Takagi-Sugeno fuzzy model. Some well-known models are used to demonstrate that the proposed method outperforms some existing methods. (C) 2017 Elsevier B.V. All rights reserved.