Recursive identification of Wiener systems with nonlinearities

Author

Zhao Ke ; Huang Lizhen ; Xiao Yongsheng ; Wang Jianhong ; Xu Qi

Author_Institution

Sch. of Inf. Eng., Nanchang Hangkong Univ., Nanchang, China

fYear

2011

fDate

15-17 April 2011

Firstpage

6030

Lastpage

6033

Abstract

This paper deals with the identification of Wiener systems with hard nonlinearities. Using the switching function, a special form of nonlinearity representation is used in the Wiener system description. Two recursive identification algorithms are proposed, one is the recursive stochastic gradient identification algorithm and the other is the adaptive forgetting through multiple models identification algorithm. Eventually the performance between these two algorithms has been compared by experiment simulations.

Keywords

control nonlinearities; gradient methods; identification; Wiener system identification; adaptive forgetting; hard nonlinearities; multiple models identification algorithm; nonlinearity representation; recursive identification; stochastic gradient identification algorithm; switching function; Adaptation model; Equations; Estimation error; Heuristic algorithms; Mathematical model; Modeling; Parameter estimation; Nonlinear systems; Recursive identification; Stochastic gradient identification; Wiener systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5778386

Filename

5778386