Strong Consistency of Kernel-Based Local Variable Selection for Nonlinear Nonparametric Systems

被引：0

作者：

Zhao, Wenxiao ^{[1
]}

Chen, Han-Fu ^{[1
]}

Bai, Er-Wei ^{[2
,3
]}

Li, Kang ^{[3
]}

机构：

[1] Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Syst & Control, Beijing 100190, Peoples R China

[2] Univ Iowa, Dept Elect & Comp Engn, Iowa City, IA 52242 USA

[3] Queens Univ, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland

来源：

2016 AUSTRALIAN CONTROL CONFERENCE (AUCC) | 2016年

基金：

英国工程与自然科学研究理事会;

关键词：

Nonlinear ARX system; variable selection; local linear estimator; strong consistency; IDENTIFICATION; LASSO; ORDER;

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：

Local variable selection by first order expansion for nonlinear nonparametric systems is investigated in the paper. By substantially modifying the algorithms developed in our earlier work, the previous results have been considerably strengthened under much less restrictive conditions. Precisely, the estimates generated by the modified algorithms are shown to have both the set and parameter convergence with probability one, rather than only the set convergence in probability given in our earlier work.

引用

页码：221 / 225

页数：5

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