Title :
H∞ bounds for the recursive-least-squares algorithm
Author :
Hassibi, Babak ; Kailath, Thomas
Author_Institution :
Inf. Syst. Lab., Stanford Univ., CA, USA
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
Keywords :
H∞ optimisation; identification; least squares approximations; recursive estimation; robust control; H∞ bounds; identification; lower bounds; model uncertainty; perturbations; prediction errors; recursive-least-squares algorithm; robust control; robustness; upper bounds; worst-case energy gain; Adaptive algorithm; Contracts; Hydrogen; Least squares approximation; Monitoring; Resonance light scattering; Riccati equations; Robustness; Uncertainty;
Conference_Titel :
Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on
Conference_Location :
Lake Buena Vista, FL
Print_ISBN :
0-7803-1968-0
DOI :
10.1109/CDC.1994.411555
