CUED Publications database

A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems

Pan, W and Yuan, Y and Gonçalves, J and Stan, GB (2016) A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems. IEEE Transactions on Automatic Control, 61. pp. 182-187. ISSN 0018-9286

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This technical note considers the identification of nonlinear discrete-time systems with additive process noise but without measurement noise. In particular, we propose a method and its associated algorithm to identify the system nonlinear functional forms and their associated parameters from a limited number of time-series data points. For this, we cast this identification problem as a sparse linear regression problem and take a Bayesian viewpoint to solve it. As such, this approach typically leads to nonconvex optimizations. We propose a convexification procedure relying on an efficient iterative re-weighted ℓ1-minimization algorithm that uses general sparsity inducing priors on the parameters of the system and marginal likelihood maximisation. Using this approach, we also show how convex constraints on the parameters can be easily added to the proposed iterative re-weighted ℓ1-minimization algorithm. In the supplementary material available online (arXiv:1408.3549), we illustrate the effectiveness of the proposed identification method on two classical systems in biology and physics, namely, a genetic repressilator network and a large scale network of interconnected Kuramoto oscillators.

Item Type: Article
Divisions: Div F > Control
Depositing User: Unnamed user with email
Date Deposited: 17 Jul 2017 19:32
Last Modified: 09 Sep 2021 02:30
DOI: 10.1109/TAC.2015.2426291