Title :
Identification of errors-in-variables model with observation outliers based on Minimum-Covariance-Determinant
Author_Institution :
King Fahd Univ. of Pet. & Miner., Dhahran
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;
Conference_Titel :
American Control Conference, 2007. ACC '07
Conference_Location :
New York, NY
Print_ISBN :
1-4244-0988-8
Electronic_ISBN :
0743-1619
DOI :
10.1109/ACC.2007.4282931
