Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant

Jaafar AlMutawa

doi:10.1016/j.jprocont.2008.11.011

Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant

Jaafar AlMutawa^*

^*Corresponding author for this work

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

15 Scopus citations

Abstract

In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.

Original language	English
Pages (from-to)	879-887
Number of pages	9
Journal	Journal of Process Control
Volume	19
Issue number	5
DOIs	https://doi.org/10.1016/j.jprocont.2008.11.011
State	Published - May 2009

Bibliographical note

Funding Information:
This research was supported by SABIC under grant FT070005. The author would like to thanks Professor Tohru Katayama for his valuable comments. The comments from the reviewer are also highly appreciated.

Keywords

Errors-in-variables
Minimum covariance determinant
Outliers
Random search algorithm
State space models
Subspace system identification

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Computer Science Applications
Industrial and Manufacturing Engineering

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10.1016/j.jprocont.2008.11.011

Cite this

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title = "Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant",

abstract = "In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.",

keywords = "Errors-in-variables, Minimum covariance determinant, Outliers, Random search algorithm, State space models, Subspace system identification",

author = "Jaafar AlMutawa",

note = "Funding Information: This research was supported by SABIC under grant FT070005. The author would like to thanks Professor Tohru Katayama for his valuable comments. The comments from the reviewer are also highly appreciated.",

year = "2009",

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doi = "10.1016/j.jprocont.2008.11.011",

language = "English",

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journal = "Journal of Process Control",

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AU - AlMutawa, Jaafar

N1 - Funding Information: This research was supported by SABIC under grant FT070005. The author would like to thanks Professor Tohru Katayama for his valuable comments. The comments from the reviewer are also highly appreciated.

PY - 2009/5

Y1 - 2009/5

N2 - In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.

AB - In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.

KW - Errors-in-variables

KW - Minimum covariance determinant

KW - Outliers

KW - Random search algorithm

KW - State space models

KW - Subspace system identification

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M3 - Article

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SN - 0959-1524

VL - 19

SP - 879

EP - 887

JO - Journal of Process Control

JF - Journal of Process Control

IS - 5

ER -

Identification of errors-in-variables state space models with observation outliers based on minimum covariance determinant

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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