Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs

Wen-Xiao Zhao; Wei Xing Zheng

Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs

Research output: Chapter in Book / Conference Paper › Conference Paper

Abstract

![CDATA[This paper is concerned with nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX), i.e., $y_{k+1}=f(y_k,cdots,y_{k+1 n_0},u_k,cdots,u_{k+1-n_0})+varepsilon_{k+1}$. Kernel functions based stochastic approximation algorithms with expanding truncations are designed for recursively estimating the value of $f(cdot)$ at any given $[y^{(1)},cdots,y^{(n_0)},u^{(1)},cdots,u^{(n_0)}]^{tau}in mathbf{R}^{2n_0}$. The estimates are shown to be strongly consistent. The NARX systems considered in this paper include the one in cite{ZhaoChen} as a special case. A numerical example is given to justify the theoretical analysis.]]

Original language	English
Title of host publication	System Identification (SYSID 2009) : a Proceedings Volume from the 15th IFAC Symposium on System Identification, Saint-Malo, France, 6-8 July, 2009
Publisher	Elsevier
Pages	1609-1614
Number of pages	6
ISBN (Print)	9783902661470
Publication status	Published - 2009
Event	IFAC Symposium on System Identification - Duration: 11 Jul 2012 → …

Conference

Conference	IFAC Symposium on System Identification
Period	11/07/12 → …

Keywords

algorithms
nonlinear systems

Cite this

@inproceedings{dd86309a62c940949322b60907c3bd5e,

title = "Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs",

abstract = "![CDATA[This paper is concerned with nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX), i.e., $y_{k+1}=f(y_k,cdots,y_{k+1 n_0},u_k,cdots,u_{k+1-n_0})+varepsilon_{k+1}$. Kernel functions based stochastic approximation algorithms with expanding truncations are designed for recursively estimating the value of $f(cdot)$ at any given $[y^{(1)},cdots,y^{(n_0)},u^{(1)},cdots,u^{(n_0)}]^{tau}in mathbf{R}^{2n_0}$. The estimates are shown to be strongly consistent. The NARX systems considered in this paper include the one in cite{ZhaoChen} as a special case. A numerical example is given to justify the theoretical analysis.]]",

keywords = "algorithms, nonlinear systems",

author = "Wen-Xiao Zhao and Zheng, {Wei Xing}",

year = "2009",

language = "English",

isbn = "9783902661470",

pages = "1609--1614",

booktitle = "System Identification (SYSID 2009) : a Proceedings Volume from the 15th IFAC Symposium on System Identification, Saint-Malo, France, 6-8 July, 2009",

publisher = "Elsevier",

note = "IFAC Symposium on System Identification ; Conference date: 11-07-2012",

}

Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs. / Zhao, Wen-Xiao; Zheng, Wei Xing.
System Identification (SYSID 2009) : a Proceedings Volume from the 15th IFAC Symposium on System Identification, Saint-Malo, France, 6-8 July, 2009. Elsevier, 2009. p. 1609-1614.

Research output: Chapter in Book / Conference Paper › Conference Paper

TY - GEN

T1 - Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs

AU - Zhao, Wen-Xiao

AU - Zheng, Wei Xing

PY - 2009

Y1 - 2009

N2 - ![CDATA[This paper is concerned with nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX), i.e., $y_{k+1}=f(y_k,cdots,y_{k+1 n_0},u_k,cdots,u_{k+1-n_0})+varepsilon_{k+1}$. Kernel functions based stochastic approximation algorithms with expanding truncations are designed for recursively estimating the value of $f(cdot)$ at any given $[y^{(1)},cdots,y^{(n_0)},u^{(1)},cdots,u^{(n_0)}]^{tau}in mathbf{R}^{2n_0}$. The estimates are shown to be strongly consistent. The NARX systems considered in this paper include the one in cite{ZhaoChen} as a special case. A numerical example is given to justify the theoretical analysis.]]

AB - ![CDATA[This paper is concerned with nonparametric identification of nonlinear autoregressive systems with exogenous inputs (NARX), i.e., $y_{k+1}=f(y_k,cdots,y_{k+1 n_0},u_k,cdots,u_{k+1-n_0})+varepsilon_{k+1}$. Kernel functions based stochastic approximation algorithms with expanding truncations are designed for recursively estimating the value of $f(cdot)$ at any given $[y^{(1)},cdots,y^{(n_0)},u^{(1)},cdots,u^{(n_0)}]^{tau}in mathbf{R}^{2n_0}$. The estimates are shown to be strongly consistent. The NARX systems considered in this paper include the one in cite{ZhaoChen} as a special case. A numerical example is given to justify the theoretical analysis.]]

KW - algorithms

KW - nonlinear systems

UR - http://handle.uws.edu.au:8081/1959.7/504591

M3 - Conference Paper

SN - 9783902661470

SP - 1609

EP - 1614

BT - System Identification (SYSID 2009) : a Proceedings Volume from the 15th IFAC Symposium on System Identification, Saint-Malo, France, 6-8 July, 2009

PB - Elsevier

T2 - IFAC Symposium on System Identification

Y2 - 11 July 2012

ER -

Nonparametric algorithms for identification of nonlinear autoregressive systems with exogenous inputs

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this