Chaotic Gravitational Search Iterative Identification for Wiener Systems

doi:10.16182/j.issn1004731x.joss.20-0352

Abstract

Abstract: The Wiener nonlinear system is composed of a dynamic linear subsystem and a series of nonlinear static subsystems, which is widely used in the fields of automatic control, chemical engineering, electrical and other fields. Considering the identification of the Wiener Output Error Autoregressive (Wiener OEAR) system, a chaotic gravitational search iterative identification algorithm is proposed, in which the chaotic optimization mechanism is introduced into the gravitational search algorithm to estimate the unknown parameters of the Wiener OEAR system and the convergence is proved. In order to show the effectiveness of the proposed identification algorithm, the gravitational search algorithm and gradient iterative algorithm are used to identify the same system, and a simulation example and an application example are given. The simulation results show that the three algorithms can effectively identify the Wiener OEAR system, and the chaotic gravitational search iterative identification is better than the gravitational search algorithm and gradient iterative algorithm in the accuracy of parameter estimation.

Key words: Wiener systems, system identification, parameter estimation, chaotic mechanism, gravitational search

CLC Number:

TP273

Xu Shanling, Li Junhong, Liu Mengru, Hua Liang. Chaotic Gravitational Search Iterative Identification for Wiener Systems[J]. Journal of System Simulation, 2021, 33(9): 2138-2146.

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