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DOKOUPIL, J. VÁCLAVEK, P.
Originální název
Design of variable exponential forgetting for estimation of the statistics of the Normal distribution
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
A recursive algorithm for estimating the statistics of the Normal distribution is designed, making it adaptive in the sense that the forgetting factor is driven by data. A mechanism to suppress obsolete information is proposed, following the principles of Bayesian decision-making. Specifically, the best combination of two time-evolution model hypotheses in terms of the geometric mean is performed. The first hypothesis assumes no change in the parameter evolution, while the second one assumes that all parameter changes are equally admitted. In order to provide data-driven forgetting, complementary probabilities assigned to each hypothesis are determined as the maximizers of the decision problem. Simulations, including a performance comparison with a recently proposed self-tuning estimator, are presented.
Klíčová slova
estimation; forgetting factor; Kullback-Leibler divergence; Normal distribution
Autoři
DOKOUPIL, J.; VÁCLAVEK, P.
Vydáno
29. 12. 2016
Nakladatel
IEEE
ISBN
978-1-5090-1837-6
Kniha
55th Conference on Decision and Control
Strany od
1179
Strany do
1184
Strany počet
6
URL
http://ieeexplore.ieee.org/document/7798426/
BibTex
@inproceedings{BUT130677, author="Jakub {Dokoupil} and Pavel {Václavek}", title="Design of variable exponential forgetting for estimation of the statistics of the Normal distribution", booktitle="55th Conference on Decision and Control", year="2016", pages="1179--1184", publisher="IEEE", doi="10.1109/CDC.2016.7798426", isbn="978-1-5090-1837-6", url="http://ieeexplore.ieee.org/document/7798426/" }