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

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 model. In order to solve the MCD problem for the EIV model we propose the random search algorithm. In addition, we show that the problem of detecting the outliers in the closed loop systems is especial case of the EIV model. The proposed algorithm has been applied to heat exchanger data.