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Errors-in-variables system identification using structural equation modeling
Uppsala University, Disciplinary Domain of Humanities and Social Sciences, Faculty of Social Sciences, Department of Statistics.
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.
2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 66, p. 218-230Article in journal (Refereed) Published
Abstract [en]

Errors-in-variables (EIV) identification refers to the problem of consistently estimating linear dynamic systems whose output and input variables are affected by additive noise. Various solutions have been presented for identifying such systems. In this study, EIV identification using Structural Equation Modeling (SEM) is considered. Two schemes for how EIV Single-Input Single-Output (SISO) systems can be formulated as SEMs are presented. The proposed formulations allow for quick implementation using standard SEM software. By simulation examples, it is shown that compared to existing procedures, here represented by the covariance matching (CM) approach, SEM-based estimation provide parameter estimates of similar quality.

Place, publisher, year, edition, pages
2016. Vol. 66, p. 218-230
Keywords [en]
System identification; Errors-in-variables models; Linear systems; Structural equation models
National Category
Control Engineering
Research subject
Statistics
Identifiers
URN: urn:nbn:se:uu:diva-277383DOI: 10.1016/j.automatica.2015.12.007ISI: 000371099300025OAI: oai:DiVA.org:uu-277383DiVA, id: diva2:904894
Funder
Swedish Research Council, 421-2011-1727Available from: 2016-01-25 Created: 2016-02-19 Last updated: 2017-11-30Bibliographically approved

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Kreiberg, DavidSöderström, TorstenYang-Wallentin, Fan

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