TY - GEN
T1 - Identification of errors-in-variables model with observation outliers based on Minimum-Covariance-Determinant
AU - Al Mutawa, J.
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Errors-invariables model
KW - Minimum covariance determinant
KW - Outliers
KW - Random search algorithm
KW - Subspace system identification
UR - https://www.scopus.com/pages/publications/46449129757
U2 - 10.1109/ACC.2007.4282931
DO - 10.1109/ACC.2007.4282931
M3 - Conference contribution
AN - SCOPUS:46449129757
SN - 1424409888
SN - 9781424409884
T3 - Proceedings of the American Control Conference
SP - 134
EP - 139
BT - Proceedings of the 2007 American Control Conference, ACC
ER -