Journal article

Non-asymptotic confidence regions for the parameters of EIV systems

M Moravej Khorasani, E Weyer

Automatica | Elsevier | Published : 2020


In this paper we consider the problem of constructing non-asymptotic confidence regions for the parameters of Errors-In-Variables (EIV) systems where both inputs and outputs are observed in noise. The Leave-out Sign-dominant Correlation Regions (LSCR) and Sign-Perturbed Sums (SPS) approaches which are two methods for constructing confidence regions from a finite number of data points, are extended to EIV systems. An appropriate correlation sequence which is required for both LSCR and SPS, is computed by a Kalman filter, and accordingly, a state-space form of the EIV system where both input and output are regarded as outputs is utilized. The constructed confidence regions include the true par..

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Awarded by Australian Research Council

Funding Acknowledgements

This work was supported by the Australian Research Council (ARC) under Discovery Grant DP130104028. The material in this paper was partially presented at the 18th IFAC Symposium on System Identification, July 9-11, 2018, Stockholm, Sweden. This paper was recommended for publication in revised form by Associate Editor Alessandro Chiuso under the direction of Editor Torsten Soderstrom.