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On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model

On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model We control a discrete-time uniformly ergodic system, which depends on an unknown parameter α0 εA, a compact set. Our purpose is to minimize the long-run average-cost functional. We estimate the unknown parameter using the biased maximum likelihood estimator and apply the control which is almost optimal for the value of estimation. This way we construct strategies such that the value of the cost functional can be arbitrarily close to the optimal value obtained for α0. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Mathematics and Optimization Springer Journals

On nearly self-optimizing strategies for a discrete-time uniformly ergodic adaptive model

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References (9)

Publisher
Springer Journals
Copyright
Copyright © 1993 by Springer-Verlag New York Inc.
Subject
Mathematics; Calculus of Variations and Optimal Control; Optimization; Systems Theory, Control; Theoretical, Mathematical and Computational Physics; Mathematical Methods in Physics; Numerical and Computational Physics, Simulation
ISSN
0095-4616
eISSN
1432-0606
DOI
10.1007/BF01195980
Publisher site
See Article on Publisher Site

Abstract

We control a discrete-time uniformly ergodic system, which depends on an unknown parameter α0 εA, a compact set. Our purpose is to minimize the long-run average-cost functional. We estimate the unknown parameter using the biased maximum likelihood estimator and apply the control which is almost optimal for the value of estimation. This way we construct strategies such that the value of the cost functional can be arbitrarily close to the optimal value obtained for α0.

Journal

Applied Mathematics and OptimizationSpringer Journals

Published: Feb 3, 2005

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