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Hybrid SVR-PSO for Identification of Nonlinear

Xianfang Wang, Zhiyong Du, Yi-xian Shen

Abstract


This paper develops a hybrid support vector regression (SVR)-particle swarm optimization (PSO) model to identify nonlinear systems. The predictive accuracy of SVR models is highly dependent on their learning parameters. Therefore, PSO is exploited to seek the optimal hyper-parameters for SVR in order to improve its generalization capability. The proposed identification procedure is successfully applied to measurements of nonlinear systems. The efficiency of the proposed algorithm was demonstrated by some simulation examples.




Keywords


Identification, Nonlinear systems, Support vector regression, Particle swarm optimization.

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