Markov chain approach to identifying Wiener systems

作     者：ZHAO WenXiao 1,2 & CHEN HanFu 1,2 1 Key Laboratory of Systems and Control,Institute of Systems Science,AMSS,Chinese Academy of Sciences,Beijing 100190,China 2 National Center for Mathematics and Interdisciplinary Sciences,Chinese Academy of Sciences,Beijing 100190,China 

作者机构：Key Laboratory of Systems and Control Institute of Systems Science AMSS Chinese Academy of Sciences Beijing China National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences Beijing China 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2012年第55卷第5期

页      面：1201-1217页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：supported by National Nature Science Foundation of China (Grant Nos. 61120106011 61134013 61104052 91130008) 

主　　题：Wiener system recursive identification stochastic approximation Markov chain strong consis-tency 

摘      要：Identification of the Wiener system composed of an infinite impulse response (IIR) linear subsystem followed by a static nonlinearity is *** recursive estimates for unknown coefficients of the linear subsystem and for the values of the nonlinear function at any fixed points are given by the stochastic approx-imation algorithms with expanding truncations (SAAWET).With the help of properties of the Markov chain connected with the linear subsystem,all estimates derived in the paper are proved to be strongly *** comparison with the existing results on the topic,the method presented in the paper simplifies the convergence analysis and requires weaker conditions.A numerical example is given,and the simulation results are consistent with the theoretical analysis.