Data-driven stabilized forgetting design using the geometric mean of normal probability densities

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F18%3APU130162" target="_blank" >RIV/00216305:26620/18:PU130162 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8619117" target="_blank" >https://ieeexplore.ieee.org/document/8619117</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC.2018.8619117" target="_blank" >10.1109/CDC.2018.8619117</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Data-driven stabilized forgetting design using the geometric mean of normal probability densities

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Data-driven stabilized forgetting design using the geometric mean of normal probability densities

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

57th Conference on Decision and Control

ISBN

978-1-5386-1394-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

1403-1408

Název nakladatele

IEEE

Místo vydání

neuveden

Místo konání akce

Miami Beach, Florida, USA

Datum konání akce

17. 12. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000458114801055