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On the stability and convergence of a sliding-window variable-regularization recursive-least-squares algorithm
Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA..
Univ Kentucky, Dept Mech Engn, 151 Ralph G Anderson Bldg, Lexington, KY 40507 USA..
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Engineering and Physics. (Matematisk modellering)
Univ Michigan, Dept Aerosp Engn, Ann Arbor, MI 48109 USA..
2016 (English)In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 5, 715-735 p.Article in journal (Refereed) PublishedText
Abstract [en]

A sliding-window variable-regularization recursive-least-squares algorithm is derived, and its convergence properties, computational complexity, and numerical stability are analyzed. The algorithm operates on a finite data window and allows for time-varying regularization in the weighting and the difference between estimates. Numerical examples are provided to compare the performance of this technique with the least mean squares and affine projection algorithms. Copyright (c) 2015 John Wiley & Sons, Ltd.

Place, publisher, year, edition, pages
John Wiley & Sons, 2016. Vol. 30, no 5, 715-735 p.
Keyword [en]
variable regularization, sliding-window RLS, digital signal processing
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kau:diva-42027DOI: 10.1002/acs.2634ISI: 000373943300003OAI: oai:DiVA.org:kau-42027DiVA: diva2:927962
Available from: 2016-05-13 Created: 2016-05-13 Last updated: 2016-07-07Bibliographically approved

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Mossberg, Magnus
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