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

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 suitabl...

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Bibliographic Details
Published in:Proceedings of the 45th IEEE Conference on Decision and Control pp. 1817 - 1822
Main Authors: Costa, A., Vazquez-Abad, F.J.
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2006
Subjects:
Adaptive control
Approximation algorithms
Convergence
Drives
Linear systems
Programmable control
Random variables
Real time systems
regime switching
stochastic approximation
Stochastic processes
tracking
USA Councils
weak convergence
ISBN:9781424401710, 1424401712
ISSN:0191-2216
Online Access:Get full text
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Summary: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
ISBN:9781424401710
1424401712
ISSN:0191-2216
DOI:10.1109/CDC.2006.377312