Recursive identification for Wiener systems using Gaussian inputs

被引：7
|
作者：
Hu, Xiao-Li [1 ]
Chen, Han-Fu [2 ]
机构：
[1] China Jiliang Univ, Dept Math, Coll Sci, Hangzhou 310018, Zhejiang, Peoples R China
[2] Chinese Acad Sci, Inst Syst Sci, Acad Math & Syst Sci, Beijing 100080, Peoples R China
来源：
ASIAN JOURNAL OF CONTROL | 2008年 / 10卷 / 03期
关键词：
Wiener system; nonparametric nonlinearity; recursive estimate; strong consistency; stochastic approximation;
D O I：
10.1002/asjc.27
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The recursive algorithms are given for identifying the single-input single-output Wiener system which consists of a moving average type linear subsystem followed by a static nonparametric nonlinearity. The input is defined to be a sequence of mutually independent Gaussian random variables. The estimates for coefficients of the linear subsystem as well as for f(upsilon) at any upsilon are proved to converge to the true values with probability one. A numerical example is given. justifying the theoretical analysis.
引用
收藏
页码：341 / 350
页数：10
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