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DOKOUPIL, J. VÁCLAVEK, P.
Originální název
Data-driven stabilized forgetting design using the geometric mean of normal probability densities
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
This paper contributes to the solution of adaptive tracking issues adopting Bayesian principles. The incomplete model of parameter variations is substituted by relaying on the use of data-suppressing procedure with two goals pursued: to provide automatic memory scheduling through the data-driven forgetting factor, and to compensate for the potential loss of persistency. The solution we propose is the geometric mean of the posterior probability density function (pdf) and its proper alternative, which, for the normal distribution, can be reduced to the convex combination of the information matrix and its regular counterpart. This coupling policy results from maximin decision-making, where the Kullback-Leibler divergence (KLD) occurs as a measure of discrepancy. In this context, the weight (probability) assigned to the information matrix is regarded as the forgetting factor and is controlled by a globally convergent Newton algorithm.
Klíčová slova
estimation; forgetting factor; Kullback-Leibler divergence; normal distribution
Autoři
DOKOUPIL, J.; VÁCLAVEK, P.
Vydáno
17. 12. 2018
Nakladatel
IEEE
ISBN
978-1-5386-1394-8
Kniha
57th Conference on Decision and Control
Strany od
1403
Strany do
1408
Strany počet
6
URL
https://ieeexplore.ieee.org/document/8619117
BibTex
@inproceedings{BUT151914, author="Jakub {Dokoupil} and Pavel {Václavek}", title="Data-driven stabilized forgetting design using the geometric mean of normal probability densities", booktitle="57th Conference on Decision and Control", year="2018", pages="1403--1408", publisher="IEEE", doi="10.1109/CDC.2018.8619117", isbn="978-1-5386-1394-8", url="https://ieeexplore.ieee.org/document/8619117" }