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H bounds for the recursive-least-squares algorithm

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2 Author(s)
Hassibi, B. ; Inf. Syst. Lab., Stanford Univ., CA, USA ; Kailath, T.

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

Published in:

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on  (Volume:4 )

Date of Conference:

14-16 Dec 1994

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