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On Model Reduction In System Identification
Division of Automatic Control, Dept of Electrical Engineering, Linköping University. (Automatic Control)ORCID iD: 0000-0002-1927-1690
1986 (English)In: Proceedings of the American Control Conference, 1986, no New York, NY, USA, 1260-1266 p.Conference paper, Published paper (Refereed)
Abstract [en]

It is shown how to use model reduction in system identification. An identification algorithm is proposed which is to be based on the least-squares identification method and either of the three model-reduction techniques: frequency-weighted L**2 model reduction, model reduction via a frequency-weighted balanced realization or frequency-weighted optimal Hankel-norm model reduction. The frequency-weighted L**2 model reduction is optimal in a minimum variance sense, while the advantage of the two other model-reduction techniques is that a consistent identification algorithm with a closed-form solution is obtained.

Place, publisher, year, edition, pages
1986. no New York, NY, USA, 1260-1266 p.
Keyword [en]
MATHEMATICAL TECHNIQUES - Least Squares Approximations, MINIMUM VARIANCE, MODEL REDUCTION, CONTROL SYSTEMS
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-55457OAI: oai:DiVA.org:kth-55457DiVA: diva2:471557
Conference
1986 American Control Conference. Seattle, WA, USA
Note
QC 20120104. Sponsors: American Automatic Control Council, Research Triangle Park, NC, U; AIAA, New York, NY, USA; AIChE, New York, NY, USA; ASME, New York, NY, USA; IEEE, New York, NY, USA; et al,Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

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Wahlberg, Bo

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