Title :
Stochastic subspace identification of linear systems with observation outliers
Author :
Almutawa, Jaafar
Author_Institution :
Fac. of Dept. of Math. & Stat., King Fahd Univ. of Pet. & Miner., Dhahran, Saudi Arabia
Abstract :
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.
Keywords :
linear systems; state-space methods; stochastic processes; time series; linear system; observation outlier; robust subspace system identification; state space model; stochastic subspace identification; time series; Computational modeling; Equations; Hafnium; Linear regression; Mathematical model; Noise; Stochastic processes;
Conference_Titel :
Control & Automation (MED), 2013 21st Mediterranean Conference on
Conference_Location :
Chania
Print_ISBN :
978-1-4799-0995-7
DOI :
10.1109/MED.2013.6608782
