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Tracking Performance Analysis of the Forgetting Factor RLS Algorithm
Academica Sinica, China.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Université Pierre et Marie Curie, France.
1992 (English)In: Proceedings of the 31st IEEE Conference on Decision and Control, 1992, 688-693 vol.1 p.Conference paper, Published paper (Refereed)
Abstract [en]

The authors present a theoretical analysis for the performance of the standard forgetting factor recursive least squares (RLS) algorithm used in the tracking of time-varying linear regression models. Under some explicit excitation conditions on the regressors, it is shown that the parameter tracking error is on the order O(μ+γ2/μ), where μ=1-λ, λ is the forgetting factor, and γ is the quantity reflecting the speed of parameter variation. Furthermore, for a large class of weakly dependent regressors, simple approximations for the covariance matrix of this error are derived. These approximations are not asymptotic in nature: they hold over all time intervals and for all μ in a certain region.

Place, publisher, year, edition, pages
1992. 688-693 vol.1 p.
Keyword [en]
Adaption, Least squares, Tracking, Recursive identification
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-91159DOI: 10.1109/CDC.1992.371639ISBN: 0-7803-0872-7 (print)OAI: oai:DiVA.org:liu-91159DiVA: diva2:617423
Conference
31st IEEE Conference on Decision and Control, Tucson, AZ, USA, December, 1992
Available from: 2013-04-23 Created: 2013-04-17 Last updated: 2013-07-30

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Ljung, Lennart

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  • nn-NB
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