Design of variable exponential forgetting for estimation of the statistics of the Normal-Wishart distribution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F16%3APU120702" target="_blank" >RIV/00216305:26620/16:PU120702 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7810676/" target="_blank" >http://ieeexplore.ieee.org/document/7810676/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ECC.2016.7810676" target="_blank" >10.1109/ECC.2016.7810676</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Design of variable exponential forgetting for estimation of the statistics of the Normal-Wishart distribution

Popis výsledku v původním jazyce

This paper addresses the adaptive estimation problem of time-varying systems in the Bayesian framework. The version of exponential forgetting with the variable factor is derived by solving the decision problem where the Kullback-Leibler divergence is used. This divergence is applied to evaluate the distance of two antagonistic model hypotheses from the model of parameter variations. The first hypothesis assumes no parameter changes, while the second one admits that the parameters may arbitrarily evolve throughout the parameter space. In this respect, the forgetting factor is interpreted as the probability that the first hypothesis meets the reality. This concept brings another technique into the class of self-tuned forgetting strategies for the discarding of obsolete information. The developed concept of forgetting is designed to complement the data learning process propagating the statistics of the Normal-Wishart distribution.

Název v anglickém jazyce

Design of variable exponential forgetting for estimation of the statistics of the Normal-Wishart distribution

Popis výsledku anglicky

This paper addresses the adaptive estimation problem of time-varying systems in the Bayesian framework. The version of exponential forgetting with the variable factor is derived by solving the decision problem where the Kullback-Leibler divergence is used. This divergence is applied to evaluate the distance of two antagonistic model hypotheses from the model of parameter variations. The first hypothesis assumes no parameter changes, while the second one admits that the parameters may arbitrarily evolve throughout the parameter space. In this respect, the forgetting factor is interpreted as the probability that the first hypothesis meets the reality. This concept brings another technique into the class of self-tuned forgetting strategies for the discarding of obsolete information. The developed concept of forgetting is designed to complement the data learning process propagating the statistics of the Normal-Wishart distribution.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/LQ1601" target="_blank" >LQ1601: CEITEC 2020</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

European Control Conference

ISBN

978-1-5090-2591-6

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

2565-2570

Název nakladatele

IEEE

Místo vydání

Neuveden

Místo konání akce

Aalborg

Datum konání akce

29. 6. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000392695300423