H " Bounds for the Recursive - Least - Squares Algorithm *
نویسنده
چکیده
We obtain upper and lower bounds for the H" norm of the RLS (Recursive-Least-Squares) algorithm. The H" norm may be regarded aa 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.
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تاریخ انتشار 2004