TY - GEN
T1 - Stochastic subspace identification of linear systems with observation outliers
AU - ALMutawa, Jaafar
PY - 2013
Y1 - 2013
N2 - We propose a diagnostic for the state space model fitting time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one.Thus we present a robust subspace system identification algorithm that is less sensitive to outliers. We give a numerical result to show effectiveness of the proposed method.
AB - We propose a diagnostic for the state space model fitting time series formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one.Thus we present a robust subspace system identification algorithm that is less sensitive to outliers. We give a numerical result to show effectiveness of the proposed method.
UR - https://www.scopus.com/pages/publications/84885207078
U2 - 10.1109/MED.2013.6608782
DO - 10.1109/MED.2013.6608782
M3 - Conference contribution
AN - SCOPUS:84885207078
SN - 9781479909971
T3 - 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings
SP - 590
EP - 596
BT - 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings
ER -