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Regularized parametric system identification:: a decision-theoretic formulation
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 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 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.ORCID iD: 0000-0001-5183-234X
2018 (English)In: Proceedings of the American Control Conference (ACC), Milwaukee, WI, USA, June, 2018., IEEE, 2018Conference paper, Published paper (Refereed)
Abstract [en]

Parametric prediction error methods constitute a classical approach to the identification of linear dynamic systems with excellent large-sample properties. A more recent regularized approach, inspired by machine learning and Bayesian methods, has also gained attention. Methods based on this approach estimate the system impulse response with excellent small-sample properties. In several applications, however, it is desirable to obtain a compact representation of the system in the form of a parametric model. By viewing the identification of such models as a decision, we develop a decision-theoretic formulation of the parametric system identification problem that bridges the gap between the classical and regularized approaches above. Using the output-error model class as an illustration, we show that this decision-theoretic approach leads to a regularized method that is robust to small sample-sizes as well as overparameterization.

Place, publisher, year, edition, pages
IEEE, 2018.
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:uu:diva-368625DOI: 10.23919/ACC.2018.8430895OAI: oai:DiVA.org:uu-368625DiVA, id: diva2:1268455
Conference
American Control Conference (ACC), June 27-29 , 2018, Milwaukee, USA
Funder
Swedish Foundation for Strategic Research , RIT15-0012Available from: 2018-12-05 Created: 2018-12-05 Last updated: 2019-03-13Bibliographically approved

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Publisher's full texthttp://acc2018.a2c2.org/http://acc2018.a2c2.org/

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Wågberg, JohanZachariah, DaveSchön, Thomas B.

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