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H^∞ bounds for the recursive-least-squares algorithm

Hassibi, Babak and Kailath, Thomas (1994) H^∞ bounds for the recursive-least-squares algorithm. In: Proceedings of the 33rd IEEE Conference on Decision and Control, 1994. Vol.4. IEEE , Piscataway, NJ, pp. 3927-3928. ISBN 0-7803-1968-0. https://resolver.caltech.edu/CaltechAUTHORS:20150219-072113191

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Abstract

We obtain upper and lower bounds for the H^∞ norm of the RLS (recursive-least-squares) algorithm. The H^∞ norm may be regarded as the worst-case energy gain from the disturbances to the prediction errors, and is therefore a measure of the robustness of an algorithm to perturbations and model uncertainty. Our results allow one to compare the robustness of RLS compared to the LMS (least-mean-squares) algorithm, which is known to minimize the H^∞ norm. Simulations are presented to show the behaviour of RLS relative to these bounds.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1109/CDC.1994.411555DOIArticle
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=411555PublisherArticle
Additional Information:© 1994 IEEE. This research was supported by the Advanced Research Projects Agency of the Department of Defense monitored by the Air Force Office of Scientific Research under Contract F4962G93-1-008.
Funders:
Funding AgencyGrant Number
Department of DefenseF4962G93-1-008
Record Number:CaltechAUTHORS:20150219-072113191
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20150219-072113191
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:54973
Collection:CaltechAUTHORS
Deposited By: Shirley Slattery
Deposited On:03 Mar 2015 02:42
Last Modified:03 Oct 2019 08:02

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