Regularized extended estimation with stabilized exponential forgetting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F17%3APU125709" target="_blank" >RIV/00216305:26620/17:PU125709 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/document/7828031/" target="_blank" >http://ieeexplore.ieee.org/document/7828031/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TAC.2017.2656379" target="_blank" >10.1109/TAC.2017.2656379</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regularized extended estimation with stabilized exponential forgetting

Popis výsledku v původním jazyce

This technical note concerns the problem of variable regularized estimation of time-varying nonlinear systems from the Bayesian viewpoint. The questions of how to impose the posterior information being variably regularized and how to forget this information are carefully discussed. The estimator design adopts the strategy of the iterated Kalman filter but differs in that, instead of the separated moments of the linearized system, only the augmented covariance matrix is updated. To suppress obsolete information, a decision problem involving the Kullback-Leibler divergence is solved. The decision provides the best combination of a pair of time-evolution model hypotheses in terms of the geometric mean. As a result, exponential forgetting with the adaptively tuned factor is inserted into the estimation process. The regularization of the investigated statistics is induced through the processing of externally supplied information. The presented estimator allows for absolute discarding or, conversely, retention of external information produced in terms of the Normal-Wishart distribution.

Název v anglickém jazyce

Regularized extended estimation with stabilized exponential forgetting

Popis výsledku anglicky

This technical note concerns the problem of variable regularized estimation of time-varying nonlinear systems from the Bayesian viewpoint. The questions of how to impose the posterior information being variably regularized and how to forget this information are carefully discussed. The estimator design adopts the strategy of the iterated Kalman filter but differs in that, instead of the separated moments of the linearized system, only the augmented covariance matrix is updated. To suppress obsolete information, a decision problem involving the Kullback-Leibler divergence is solved. The decision provides the best combination of a pair of time-evolution model hypotheses in terms of the geometric mean. As a result, exponential forgetting with the adaptively tuned factor is inserted into the estimation process. The regularization of the investigated statistics is induced through the processing of externally supplied information. The presented estimator allows for absolute discarding or, conversely, retention of external information produced in terms of the Normal-Wishart distribution.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

20205 - Automation and control systems

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

2017

Kód důvěrnosti údajů

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

Název periodika

IEEE TRANSACTIONS ON AUTOMATIC CONTROL

ISSN

0018-9286

e-ISSN

1558-2523

Svazek periodika

62

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

8

Strana od-do

6513-6520

Kód UT WoS článku

000417090000041

EID výsledku v databázi Scopus

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