Identification of errors-in-variables model with observation outliers based on Minimum-Covariance-Determinant

Author

AlMutawa, J.

Author_Institution

King Fahd Univ. of Pet. & Miner., Dhahran

fYear

2007

fDate

9-13 July 2007

Firstpage

134

Lastpage

139

Abstract

In this paper, we develop a subspace system identification algorithm for the errors-in-variables (EIV) model subject to observation noise with outliers. By using the minimum covariance determinant (MCD), we identify and delete the outliers, and then apply the classical EIV subspace system identification algorithms to get state space models. In order to solve the MCD problem for the EIV model we propose a random search algorithm. The proposed algorithm has been applied to a heat exchanger data.

Keywords

covariance matrices; identification; regression analysis; search problems; state-space methods; EIV subspace system identification algorithms; MCD problem; covariance matrix; errors-in-variables model; minimum-covariance-determinant; multivariate linear regression model; observation noise; observation outliers; random search algorithm; state space models; Cities and towns; Earthquakes; Error correction; Gaussian noise; Least squares approximation; Linear regression; Minerals; Petroleum; State-space methods; System identification; Subspace system identification; errors-in-variables model; minimum covariance determinant; outliers; random search algorithm;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2007. ACC '07

Conference_Location

New York, NY

ISSN

0743-1619

Print_ISBN

1-4244-0988-8

Electronic_ISBN

0743-1619

Type

conf

DOI

10.1109/ACC.2007.4282931

Filename

4282931