Article (Scientific journals)
A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
PAN, Wei; Yuan, Ye; GONCALVES, Jorge et al.
2016In IEEE Transaction on Automatic Control, 61 (1), p. 182-187
Peer reviewed
 

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Abstract :
[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.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Systems Control (Goncalves Group)
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
PAN, Wei 
Yuan, Ye
GONCALVES, Jorge ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Stan, Guy-Bart
External co-authors :
yes
Language :
English
Title :
A Sparse Bayesian Approach to the Identification of Nonlinear State-Space Systems
Publication date :
January 2016
Journal title :
IEEE Transaction on Automatic Control
Volume :
61
Issue :
1
Pages :
182-187
Peer reviewed :
Peer reviewed
Commentary :
arXiv:1408.3549
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