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Extended accuracy analysis of a covariance matching approach for identifying errors-in-variables systems
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Automatic control.
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
2014 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 50, no 10, 2597-2605 p.Article in journal (Refereed) Published
Abstract [en]

A covariance matching approach for identifying errors-in-variables systems is analyzed for the general case. The asymptotic covariance matrix of the jointly estimated system parameters, noise variances and auxiliary parameters is derived. An algorithm for how to compute this covariance matrix from given system descriptions is also provided. The results generalize previous known special cases. Using Monte Carlo analysis, we illustrate the proposed algorithm. The results suggest close agreement between the theoretical and empirical accuracy.

Place, publisher, year, edition, pages
2014. Vol. 50, no 10, 2597-2605 p.
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Control Engineering
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URN: urn:nbn:se:uu:diva-238583DOI: 10.1016/j.automatica.2014.08.020ISI: 000344207300018OAI: oai:DiVA.org:uu-238583DiVA: diva2:772372
Available from: 2014-09-16 Created: 2014-12-14 Last updated: 2017-12-05Bibliographically approved

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Söderström, TorstenKreiberg, David

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