Reference : A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
http://hdl.handle.net/10993/20778
A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
English
Pan, Wei []
Yuan, Ye []
Goncalves, Jorge mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > >]
Stan, Guy-Bart []
Jan-2016
IEEE Transaction on Automatic Control
61
1
182-187
Yes
[en] 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 optimisations. We propose a convexification procedure relying on an efficient iterative re-weighted ℓ1-minimisation 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 our proposed iterative re-weighted ℓ1-minimisation algorithm. In the supplementary material \cite{appendix}, 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.
Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
http://hdl.handle.net/10993/20778
arXiv:1408.3549

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Limited access
1408.3549v4.pdfAuthor preprint407.1 kBRequest a copy

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.