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Design Variables For Bias Distribution In Transfer Function Eestimation
Department of Electrical Engineering, Linköping University. (Automatic Control)ORCID iD: 0000-0002-1927-1690
Department of Electrical Engineering, Linköping University. (Automatic Control)
1986 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, ISSN 0018-9286, Vol. AC-31, no 2, 134-144 p.Article in journal (Refereed) Published
Abstract [en]

Estimation of transfer functions of linear systems is one of the most common system identification problems. Several different design variables, chosen by the user for the identification procedure, affect the properties of the resulting estimate. The way in which the choices of prefilters, noise models, sampling interval, and prediction horizon (the use of k-step ahead prediction methods) influence the estimate is discussed. An important aspect is that the true system is not assumed to be exactly represented within the chosen model set. The estimate will thus be biased. It is shown how the distribution of bias in the frequency domain is governed by a weighting function, which emphasizes different frequency bands. The weighting function, in turn, is a result of the previously listed design variables. It is shown that the common least-squares method has a tendency to emphasize high frequencies and that this can be counteracted by prefiltering. It is also shown that, asymptotically, it is only the prediction horizon itself, and not how it is split up into sampling interval times number of predicted sampling instants, that affects this weighting function.

Place, publisher, year, edition, pages
1986. Vol. AC-31, no 2, 134-144 p.
Keyword [en]
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-55455DOI: 10.1109/TAC.1986.1104221OAI: diva2:471563
QC 20120104Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

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Wahlberg, Bo
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