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Consistent Low-Complexity Estimation of Active Parameters in Large Linear Regressions
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
1997 (English)In: Proceedings of the 11th IFAC Symposium on System Identification, 1997, Vol. 3, 1419-1424 p.Conference paper (Refereed)
Abstract [en]

Some important practical signals and systems can be modeled by very large linear regression models where it is reasonable that most of the parameters are zero. We give an efficient method to solve this combined estimation and structure determination problem. It is related to Akaike-like criteria, and is based on one LMS filter and thus it is of low complexity. Asymptotic analysis shows that the method is consistent for finite impulse response models. A recursive algorithm is derived, which can be applied to time-varying systems as well. An example shows the efficiency of the approach.

Place, publisher, year, edition, pages
1997. Vol. 3, 1419-1424 p.
Keyword [en]
Systems, Linear regression models, Estimation, Determination problem, Recursive algorithm
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-93790ISBN: 0080425925OAI: diva2:627949
11th IFAC Symposium on System Identification, Fukuoka, Japan, July, 1997
Available from: 2013-06-13 Created: 2013-06-10 Last updated: 2013-06-13

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Gustafsson, Fredrik
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Automatic ControlThe Institute of Technology
Control Engineering

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