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A covariance matching approach for identifying errors-in-variables systems
Division of Systems and Control, Department of Information Technology, Uppsala University.
Karlstad University, Faculty of Technology and Science, Department of Physics and Electrical Engineering.
Division of Systems and Control, Department of Information Technology, Uppsala University.
2009 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 45, no 9, p. 2018-2031Article in journal (Refereed) Published
Abstract [en]

The errors-in-variables identification problem concerns dynamic systems whose input and output variables are affected by additive noise. Several estimation methods have been proposed for identifying dynamic errors-in-variables models. In this paper a covariance matching approach is proposed to solve the identification problem. It applies for general types of input signals. The method utilizes a small set of covariances of the measured input–output data. This property applies also for some other methods, such as the Frisch scheme and the bias-eliminating least squares method. Algorithmic details for the proposed method are provided. User choices, for example specification of which input–output covariances to utilize, are discussed in some detail. The method is evaluated by using numerical examples, and is shown to have competitive properties as compared to alternative methods.

Place, publisher, year, edition, pages
2009. Vol. 45, no 9, p. 2018-2031
Keywords [en]
System identification, Errors-in-variables models, Linear systems, Covariance functions
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kau:diva-11119DOI: 10.1016/j.automatica.2009.05.010OAI: oai:DiVA.org:kau-11119DiVA, id: diva2:494686
Available from: 2012-02-08 Created: 2012-02-08 Last updated: 2017-12-07Bibliographically approved

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