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On Data Preprocessing for Subspace Methods
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2000 (English)In: Proceedings of the 39th IEEE Conference on Decision and Control, IEEE , 2000, Vol. 3, 2403-2408 p.Conference paper (Refereed)
Abstract [en]

In modern data analysis often the first step is to perform some data preprocessing, e.g. detrending or elimination of periodic components of known period length. This is normally done using least squares regression. Only afterwards black box models are estimated using either pseudo-maximum-likelihood methods, prediction error methods or subspace algorithms. In this paper it is shown, that for subspace methods this is essentially the same as including the corresponding input variables, e.g. a constant or a trend or a periodic component, as additional input variables. Here essentially means, that the estimates only dier through the choice of initial values.

Place, publisher, year, edition, pages
IEEE , 2000. Vol. 3, 2403-2408 p.
Keyword [en]
Subspace algorithms, Linear systems, Estimation, Identification
National Category
Engineering and Technology Control Engineering
URN: urn:nbn:se:liu:diva-90831DOI: 10.1109/CDC.2000.914159ISBN: 0-7803-6638-7OAI: diva2:616254
39th IEEE Conference on Decision and Control, Sydney, Australia, 12-15 December 2000
Available from: 2013-04-15 Created: 2013-04-07 Last updated: 2015-02-24

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