Title :
Identification of Wiener nonlinear systems using the key-term separation principle and the filtering approach
Author :
Zhenwei, Shi ; Zhicheng, Ji
Author_Institution :
School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, P.R. China
Abstract :
A Wiener finite impulse response moving average (FIR-MA) model is rewritten as a standard regression controlled moving average form by applying the key-term separation principle. A filtering based recursive least squares estimation algorithm and a maximum likelihood recursive least squares estimation algorithm are developed for the parameter identification of Wiener FIR-MA model. Both of the proposed algorithms could reduce the influence of colored noise in parameters estimation through the data filtering approach; further improve the accuracy of parameters estimation. The simulation results demonstrate the efficiency of two proposed algorithms.
Keywords :
Data models; Filtering; Least squares approximations; Maximum likelihood estimation; Nonlinear systems; Parameter estimation; Filtering theory; Key term; Maximum likelihood; Parameter estimation; Recursive least squares; Wiener systems;
Conference_Titel :
Control Conference (CCC), 2015 34th Chinese
Conference_Location :
Hangzhou, China
DOI :
10.1109/ChiCC.2015.7259919