Publication detail

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

DOKOUPIL, J. VÁCLAVEK, P.

Original Title

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

Type

conference paper

Language

English

Original Abstract

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.

Keywords

ARARX system; Variational Bayes method; normal-Wishart distribution

Authors

DOKOUPIL, J.; VÁCLAVEK, P.

Released

13. 12. 2023

Publisher

IEEE

Location

NEW YORK

ISBN

979-8-3503-0124-3

Book

62th IEEE Conference on Decision and Control

Pages from

4215

Pages to

4222

Pages count

8

URL

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

Full text in the Digital Library

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"
}