Title: Stochastic subspace identification of linear systems with observation outliers
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.
Publication Year: 2013
Publication Date: 2013-06-01
Language: en
Type: article
Indexed In: ['crossref']
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