A fuzzy C‐regression model algorithm using a new PSO algorithm

• Published in: International journal of adaptive control and signal processing Vol. 32; no. 1; pp. 115 - 133
• Main Authors: Taieb, Adel, Soltani, Moez, Chaari, Abdelkader
• Format: Journal Article
• Language: English
• Published: Bognor Regis Wiley Subscription Services, Inc 01.01.2018
• Subjects: Algorithms; chaos adaptive particle swarm optimization; Clustering; fuzzy C‐regression model clustering algorithm; identification; multiple‐input multiple‐output; Nonlinear systems; Parameter identification; Particle swarm optimization; Regression models; Takagi‐Sugeno fuzzy models
• ISSN: 0890-6327; 1099-1115
• DOI: 10.1002/acs.2829

Summary In this paper, a new methodology is introduced for the identification of the parameters of the multiple‐input–multiple‐output local linear Takagi‐Sugeno fuzzy models using the weighted recursive least squares (WRLS). The WRLS is sensitive to initialization, which leads to no convergence. In order to overcome this problem, adaptive chaos particle swarm optimization is proposed to optimize the initial states of WRLS. This new algorithm is improved versions of the original particle swarm optimization algorithm. Finally, comparative experiments are designed to verify the validity of the proposed clustering algorithm and the Takagi‐Sugeno fuzzy model identification method, and the results show that the new method is effective in describing a complicated nonlinear system with significantly high accuracies compared with approaches in the literature.