Levy-particle swarm optimization intelligent search-based iterative identification for nonparametric models of bilinear systems with Gaussian mixture noises

Abstract

Motivated by the fact that the model-based robust identification has not been studied extensively, a novel iterative identification method is proposed for the nonparametric model of bilinear systems with Gaussian mixture noises. The proposed method is robust to the impulsive disturbances, and combines intelligent search techniques with robust estimation theories. Firstly, the block-oriented Wiener-Hammerstein model is adopted to achieve the fast modeling, and the Levy-particle swarm optimization intelligent search is exploited for the parameter estimation. Secondly, according to re-descending M-estimators, the weighted cost function is designed to suppress the influence of outliers. Thirdly, based on the Levy-PSO intelligent search and the weighted cost function, the iterative identification procedure is conducted to obtain the robust and accurate parameter estimates. Finally, the simulation tests demonstrate the effectiveness of the proposed model and the proposed method.