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A fuzzy C-regression model algorithm using a new PSO algorithm
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DOI：10.1002/acs.2829.png)
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Abstract
En 中文
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.
Keywords:
chaos adaptive particle swarm optimization
fuzzy C-regression model clustering algorithm
identification
multiple-input multiple-output
Takagi-Sugeno fuzzy models
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