Unifying some higher-order statistic-based methods for errors-in-variables model identification

Stéphane Thil; Wei Xing Zheng; Marion Gilson; Hugues Garnier

doi:10.1016/j.automatica.2009.04.019

Unifying some higher-order statistic-based methods for errors-in-variables model identification

Stéphane Thil, Wei Xing Zheng, Marion Gilson, Hugues Garnier

School of Computer, Data & Mathematical Sciences

Research output: Contribution to journal › Article

10 Citations (Scopus)

Abstract

In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.

Original language	English
Pages (from-to)	1937-1942
Number of pages	6
Journal	Automatica
Volume	45
Issue number	8
DOIs	https://doi.org/10.1016/j.automatica.2009.04.019
Publication status	Published - 2009

Keywords

automaton
linear time invariant systems

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10.1016/j.automatica.2009.04.019

Cite this

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title = "Unifying some higher-order statistic-based methods for errors-in-variables model identification",

abstract = "In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.",

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T1 - Unifying some higher-order statistic-based methods for errors-in-variables model identification

AU - Thil, Stéphane

AU - Zheng, Wei Xing

AU - Gilson, Marion

AU - Garnier, Hugues

PY - 2009

Y1 - 2009

N2 - In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.

AB - In this paper, the problem of identifying linear discrete-time systems from noisy input and output data is addressed. Several existing methods based on higher-order statistics are presented. It is shown that they stem from the same set of equations and can thus be united from the viewpoint of extended instrumental variable methods. A numerical example is presented which confirms the theoretical results. Some possible extensions of the methods are then given.

KW - automaton

KW - linear time invariant systems

UR - http://handle.uws.edu.au:8081/1959.7/502614

U2 - 10.1016/j.automatica.2009.04.019

DO - 10.1016/j.automatica.2009.04.019

M3 - Article

SN - 0005-1098

VL - 45

SP - 1937

EP - 1942

JO - Automatica

JF - Automatica

IS - 8

ER -

Unifying some higher-order statistic-based methods for errors-in-variables model identification

Abstract

Keywords

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