Strong consistency of kernel-based local variable selection for nonlinear nonparametric systems

Wenxiao Zhao; Han-Fu Chen; Er-Wei Bai; Kang Li

doi:10.1109/AUCC.2016.7868192

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Conference proceeding

Strong consistency of kernel-based local variable selection for nonlinear nonparametric systems

Wenxiao Zhao, Han-Fu Chen, Er-Wei Bai and Kang Li

2016 Australian Control Conference (AuCC), pp.221-225

11/2016

DOI: 10.1109/AUCC.2016.7868192

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Abstract

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.

Approximation algorithms

Convergence

Convex functions

Input variables

Kernel

local linear estimator

Nonlinear ARX system

Nonlinear systems

Signal processing algorithms

strong consistency

variable selection

Details

Title: Subtitle: Strong consistency of kernel-based local variable selection for nonlinear nonparametric systems
Creators: Wenxiao Zhao - Chinese Academy of Sciences
Han-Fu Chen - Key Lab. of Syst. & Control, Acad. of Math. & Syst. Sci., Beijing, China
Er-Wei Bai - University of Iowa
Kang Li - Queen's University Belfast
Resource Type: Conference proceeding
Publication Details: 2016 Australian Control Conference (AuCC), pp.221-225
DOI: 10.1109/AUCC.2016.7868192
Publisher: Engineers Australia
Language: English
Date published: 11/2016
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197560202771

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