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arXiv · 2204.00769

Variational message passing for online polynomial NARMAX identification

Wouter Kouw·Albert Podusenko·Magnus Koudahl·Maarten Schoukens
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Abstract

We propose a variational Bayesian inference procedure for online nonlinear system identification. For each output observation, a set of parameter posterior distributions is updated, which is then used to form a posterior predictive distribution for future outputs. We focus on the class of polynomial NARMAX models, which we cast into probabilistic form and represent in terms of a Forney-style factor graph. Inference in this graph is efficiently performed by a variational message passing algorithm. We show empirically that our variational Bayesian estimator outperforms an online recursive least-squares estimator, most notably in small sample size settings and low noise regimes, and performs on par with an iterative least-squares estimator trained offline.

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Wouter Kouw, Albert Podusenko, Magnus Koudahl, Maarten Schoukens. 2022-04-02. Variational message passing for online polynomial NARMAX identification. https://arxiv.org/abs/2204.00769

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