Nonparametric Identification of Affine Nonlinear Systems

被引：0

作者：

Chen, Xing-Min ^{[1
]}

Gao, Chao ^{[2
]}

Wang, Lei ^{[1
]}

机构：

[1] Dalian Univ Technol, Sch Math Sci, Dalian 116024, Peoples R China

[2] Beijing Inst Informat & Control, Beijing 100037, Peoples R China

来源：

2013 32ND CHINESE CONTROL CONFERENCE (CCC) | 2013年

基金：

中国国家自然科学基金;

关键词：

Affine nonlinear system; Nonparametric kernel regression estimation; Recursive identification; Markov chain; Strong consistency; ADAPTIVE-CONTROL; HAMMERSTEIN;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Recursive nonparametric identification of affine nonlinear systems is considered in this paper. First, by using Markov chain approach, the geometric ergodicity is established for the affine nonlinear system under suitable conditions with the help of the concept of Q-geometric ergodicity. Then recursive local constant kernel regression estimator is proposed for estimating the values of the nonlinear functions at fixed points. It is proved that the estimate converges to the true value with probability one. Finally a simulation example is provided to justify the theoretical analysis.

引用

页码：2007 / 2011

页数：5

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