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Adaptive stepsize selection for tracking in a non-stationary environment: a new pre-emptive approach

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2 Author(s)
Costa, A. ; ARC Centre of Excellence for Math. & Stat. of Complex Syst., Melbourne Univ., Vic. ; Vazquez-Abad, F.J.

We consider the problem of using a stochastic approximation algorithm to perform online tracking in a non-stationary environment characterized by infrequent and sudden "regime changes". The primary contribution of this paper is a new approach for adaptive stepsize selection that is suitable for this type of non-stationarity. Our approach is pre-emptive rather than reactive, and is based on a strategy of maximising the rate of adaptation, subject to a constraint on the probability that the iterates fall outside a pre-determined range of "acceptable error". The theoretical basis for our approach is provided by the theory of weak convergence for stochastic approximation algorithms

Published in:

Decision and Control, 2006 45th IEEE Conference on

Date of Conference:

13-15 Dec. 2006