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A novel identification method for Takagi-Sugeno fuzzy model. (English) Zbl 1400.93319
Summary: 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.
MSC:
93E12 Identification in stochastic control theory
93C42 Fuzzy control/observation systems
90C59 Approximation methods and heuristics in mathematical programming
90C90 Applications of mathematical programming
93E24 Least squares and related methods for stochastic control systems
Software:
SparseFIS; ANFIS
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