Regularized estimation with variable exponential forgetting

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F20%3APU138218" target="_blank" >RIV/00216305:26620/20:PU138218 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9304385" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9304385</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC42340.2020.9304385" target="_blank" >10.1109/CDC42340.2020.9304385</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Regularized estimation with variable exponential forgetting

Popis výsledku v původním jazyce

The real-time estimation of normal regression-type models with unknown time-varying parameters is considered and discussed from the Bayesian perspective. A novel tracking technique combining the variable regularization approach with the forgetting operation is derived and elaborated into algorithmic details. The regularization of the parameter covariance is accomplished by incorporating soft equality constraints on the regression parameters into the learning procedure. The resultant procedure is designed to smooth the parameter estimate, preventing it from changing too rapidly. Moreover, the form of the constraints guarantees a minimal amount of information about the parameter estimate, which makes the estimator robust with respect to poor system excitation. The forgetting of obsolete information is provided in two different parameterization options and is performed automatically in a way that complies with the degree of the process nonstationarity. The whole concept preserves the selfreproducibility of the statistics of the normal-Wishart distribution.

Název v anglickém jazyce

Regularized estimation with variable exponential forgetting

Popis výsledku anglicky

The real-time estimation of normal regression-type models with unknown time-varying parameters is considered and discussed from the Bayesian perspective. A novel tracking technique combining the variable regularization approach with the forgetting operation is derived and elaborated into algorithmic details. The regularization of the parameter covariance is accomplished by incorporating soft equality constraints on the regression parameters into the learning procedure. The resultant procedure is designed to smooth the parameter estimate, preventing it from changing too rapidly. Moreover, the form of the constraints guarantees a minimal amount of information about the parameter estimate, which makes the estimator robust with respect to poor system excitation. The forgetting of obsolete information is provided in two different parameterization options and is performed automatically in a way that complies with the degree of the process nonstationarity. The whole concept preserves the selfreproducibility of the statistics of the normal-Wishart distribution.

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

2020

Kód důvěrnosti údajů

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

Název statě ve sborníku

59th Conference on Decision and Control

ISBN

978-1-7281-7446-4

ISSN

—

e-ISSN

—

Počet stran výsledku

7

Strana od-do

312-318

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Jeju Island, Republic of Korea

Datum konání akce

14. 12. 2020

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000717663400040