Name
Abstract | ||
|---|---|---|
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 reweighted `1-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 the proposed iterative re-weighted `1-minimisation algorithm. In the supplementary material [1], 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TAC.2015.2426291 | Automatic Control, IEEE Transactions |
Keywords | Field | DocType |
Optimization,Bayes methods,Time series analysis,Linear programming,Linear regression,Nonlinear systems | Mathematical optimization,Nonlinear system,Control theory,Nonlinear system identification,Marginal likelihood,Linear programming,Graphical model,Prior probability,State space,Mathematics,Parameter identification problem | Journal |
Volume | Issue | ISSN |
PP | 99 | 0018-9286 |
Citations | PageRank | References |
16 | 0.97 | 20 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weidong Pan | 1 | 48 | 7.23 |
| Ye Yuan | 2 | 438 | 61.04 |
| Goncalves, J. | 3 | 404 | 42.24 |
| Guy-bart Stan | 4 | 281 | 26.54 |