Recursive identification of the ARARX model based on the variational Bayes method
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Date
2023-12-13
Authors
Dokoupil, Jakub
Václavek, Pavel
0000-0001-7505-8571Advisor
Referee
Mark
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Publisher
IEEE
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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.
Description
Citation
62th IEEE Conference on Decision and Control. 2023, p. 4215-4222.
https://ieeexplore.ieee.org/document/10383518
https://ieeexplore.ieee.org/document/10383518
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Peer-reviewed
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Accepted version
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en
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Defence
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(C) IEEE