Kernel-based local order estimation of nonlinear nonparametric systems

被引：24
|
作者：
Zhao, Wenxiao [1 ]
Chen, Han-Fu [1 ]
Bai, Er-wei [2 ]
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：
AUTOMATICA | 2015年 / 51卷
基金：
英国工程与自然科学研究理事会; 美国国家科学基金会;
关键词：
Nonlinear ARX system; Recursive local linear estimator; Order estimation; Strong consistency; MODEL ORDER; IDENTIFICATION; SELECTION; OPTIMIZATION;
D O I：
10.1016/j.automatica.2014.10.069
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider the local order estimation of nonlinear autoregressive systems with exogenous inputs (NARX), which may have different local dimensions at different points. By minimizing the kernel-based local information criterion introduced in this paper, the strongly consistent estimates for the local orders of the NARX system at points of interest are obtained. The modification of the criterion and a simple procedure of searching the minimum of the criterion, are also discussed. The theoretical results derived here are tested by simulation examples. (C) 2014 Elsevier Ltd. All rights reserved.
引用
收藏
页码：243 / 254
页数：12
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