Detail publikace

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

DOKOUPIL, J. VÁCLAVEK, P.

Originální název

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

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

ARARX system; Variational Bayes method; normal-Wishart distribution

Autoři

DOKOUPIL, J.; VÁCLAVEK, P.

Vydáno

13. 12. 2023

Nakladatel

IEEE

Místo

NEW YORK

ISBN

979-8-3503-0124-3

Kniha

62th IEEE Conference on Decision and Control

Strany od

4215

Strany do

4222

Strany počet

8

URL

https://ieeexplore.ieee.org/document/10383518

Plný text v Digitální knihovně

http://hdl.handle.net/11012/244722

BibTex

@inproceedings{BUT186745,
  author="Jakub {Dokoupil} and Pavel {Václavek}",
  title="Recursive identification of the ARARX model based on the variational Bayes method",
  booktitle="62th IEEE Conference on Decision and Control",
  year="2023",
  pages="4215--4222",
  publisher="IEEE",
  address="NEW YORK",
  doi="10.1109/CDC49753.2023.10383518",
  isbn="979-8-3503-0124-3",
  url="https://ieeexplore.ieee.org/document/10383518"
}