DocumentCode :
3294621
Title :
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
Link To Document :
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