Recursive identification of the ARARX model based on the variational Bayes method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F23%3APU149911" target="_blank" >RIV/00216305:26620/23:PU149911 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10383518" target="_blank" >https://ieeexplore.ieee.org/document/10383518</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/CDC49753.2023.10383518" target="_blank" >10.1109/CDC49753.2023.10383518</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Recursive identification of the ARARX model based on the variational Bayes method

Popis výsledku v původním jazyce

Bayesian parameter estimation of autoregressive (AR) with exogenous input (X) systems in the presence of colored model noise is addressed. The stochastic system under consideration is driven by colored noise that arises from passing an initially white noise through an AR filter. Owing to the additional AR filter, the ARARX schema provides more flexibility than the ARX one. The gained flexibility is countered by the fact that the ARARX system is no longer linear-in-parameters unless the white noise components or the AR noise filter are available. This paper analyzes the problem of estimating the unknown coefficients of the ARARX system and the model noise precision under conditions where the AR noise filter is both available and unavailable. While the former condition reduces the estimation problem to standard linear least squares, the latter one gives rise to an analytically intractable estimation problem. The intractability is resolved by the distributional approximation technique based on the variational Bayes (VB) method.

Název v anglickém jazyce

Recursive identification of the ARARX model based on the variational Bayes method

Popis výsledku anglicky

Bayesian parameter estimation of autoregressive (AR) with exogenous input (X) systems in the presence of colored model noise is addressed. The stochastic system under consideration is driven by colored noise that arises from passing an initially white noise through an AR filter. Owing to the additional AR filter, the ARARX schema provides more flexibility than the ARX one. The gained flexibility is countered by the fact that the ARARX system is no longer linear-in-parameters unless the white noise components or the AR noise filter are available. This paper analyzes the problem of estimating the unknown coefficients of the ARARX system and the model noise precision under conditions where the AR noise filter is both available and unavailable. While the former condition reduces the estimation problem to standard linear least squares, the latter one gives rise to an analytically intractable estimation problem. The intractability is resolved by the distributional approximation technique based on the variational Bayes (VB) method.

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

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

62th IEEE Conference on Decision and Control

ISBN

979-8-3503-0124-3

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

4215-4222

Název nakladatele

IEEE

Místo vydání

NEW YORK

Místo konání akce

Singapur

Datum konání akce

13. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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