access icon free Diagnostics subspace identification method of linear state-space model with observation outliers

© The Institution of Engineering and Technology
Received 12/03/2015, Accepted 06/08/2016, Revised 22/01/2016, Published 10/08/2016

The authors propose a diagnostic technique for the state-space model fitting of time series by deleting some observations and measuring the change in the parameter estimates. They consider this approach in order to distinguish an observational outlier from an innovational one. Thus, they present a robust subspace identification algorithm that is less sensitive to outliers. A Monte Carlo simulation for a vibrating structure model demonstrates the effectiveness of the proposed algorithm and its ability to detect outliers in the measurements as well as the dynamical state.

Inspec keywords: parameter estimation; linear systems; Monte Carlo methods; time series; state-space methods

Other keywords: observation outliers; Monte Carlo simulation; parameter estimation; linear state-space model; dynamical state; time series; vibrating structure model; diagnostics subspace identification method

Subjects: Linear control systems; Simulation, modelling and identification; Monte Carlo methods; Control system analysis and synthesis methods

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