Tracking Performance Analysis of the Forgetting Factor RLS Algorithm
1992 (English)In: Proceedings of the 31st IEEE Conference on Decision and Control, 1992, 688-693 vol.1 p.Conference paper (Refereed)
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.
Adaption, Least squares, Tracking, Recursive identification
IdentifiersURN: urn:nbn:se:liu:diva-91159DOI: 10.1109/CDC.1992.371639ISBN: 0-7803-0872-7OAI: oai:DiVA.org:liu-91159DiVA: diva2:617423
31st IEEE Conference on Decision and Control, Tucson, AZ, USA, December, 1992