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Model reductions of high-order estimated models: the asymptotic ML approach
Department of Electrical Engineering, Linköping University. (Automatic Control)ORCID iD: 0000-0002-1927-1690
1989 (English)In: International Journal of Control, ISSN 0020-7179, E-ISSN 1366-5820, Vol. 49, no 1, 169-192 p.Article in journal (Refereed) Published
Abstract [en]

The reduction of order of high-order models obtained from an identification experiment is discussed from a statistical point of view. The asymptotic maximum likelihood (ML) approach is defined to reduce the order of an estimated model. This approach considers the maximum likelihood criterion given the asymptotic statistics of the estimated model, and corresponds to frequency weighted L2-norm model reduction. By using the insight from the asymptotic ML approach, an identification algorithm is proposed based on a high-order ARX estimate and model reduction via a frequency weighted balanced realization. The advantage of this algorithm is that iterative minimization methods are not required to find the estimate.

Place, publisher, year, edition, pages
1989. Vol. 49, no 1, 169-192 p.
Keyword [en]
Statistical Methods--Estimation, High-Order Models, Identification Algorithm, Maximum Likelihood, Model Reductions, Control Systems
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-55453DOI: 10.1080/00207178908559628ISI: A1989T275500014OAI: oai:DiVA.org:kth-55453DiVA: diva2:471558
Note
QC 20120104Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2017-12-08Bibliographically approved

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Wahlberg, Bo

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