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Sparse Bayesian Nonlinear System Identification using Variational Inference

Jacobs, W.R., Baldacchino, T., DODD, Tony and Anderson, S.R. (2018) Sparse Bayesian Nonlinear System Identification using Variational Inference. IEEE Transactions on Automatic Control. ISSN 0018-9286

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Abstract or description

IEEE Bayesian nonlinear system identification for one of the major classes of dynamic model, the nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied to date. Markov chain Monte Carlo (MCMC) methods have been developed, which tend to be accurate but can also be slow to converge. In this contribution, we present a novel, computationally efficient solution to sparse Bayesian identification of the NARX model using variational inference, which is orders of magnitude faster than MCMC methods. A sparsity-inducing hyper-prior is used to solve the structure detection problem. Key results include: 1. successful demonstration of the method on low signal-to-noise ratio signals (down to 2dB); 2. successful benchmarking in terms of speed and accuracy against a number of other algorithms: Bayesian LASSO, reversible jump MCMC, forward regression orthogonalisation, LASSO and simulation error minimisation with pruning; 3. accurate identification of a real world system, an electroactive polymer; and 4. demonstration for the first time of numerically propagating the estimated nonlinear time-domain model parameter uncertainty into the frequency-domain.

Item Type: Article
Additional Information: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy.
Uncontrolled Keywords: Bayesian estimation; variational inference; system identification; NARX model
Faculty: School of Creative Arts and Engineering > Engineering
Depositing User: Library STORE team
Date Deposited: 15 Jul 2020 14:39
Last Modified: 15 Dec 2020 16:41
Related URLs:
URI: http://eprints.staffs.ac.uk/id/eprint/6231

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