Global Optimization by Multilevel Coordinate Search
Journal of Global Optimization
Recursive identification of switched ARX systems
Automatica (Journal of IFAC)
Switched and PieceWise Nonlinear Hybrid System Identification
HSCC '08 Proceedings of the 11th international workshop on Hybrid Systems: Computation and Control
Identification of deterministic switched ARX systems via identification of algebraic varieties
HSCC'05 Proceedings of the 8th international conference on Hybrid Systems: computation and control
A clustering technique for the identification of piecewise affine systems
Automatica (Journal of IFAC)
Identification of piecewise affine systems based on statistical clustering technique
Automatica (Journal of IFAC)
Identification of piecewise affine systems via mixed-integer programming
Automatica (Journal of IFAC)
Identification of switched linear regression models using sum-of-norms regularization
Automatica (Journal of IFAC)
Learning nonlinear hybrid systems: from sparse optimization to support vector regression
Proceedings of the 16th international conference on Hybrid systems: computation and control
Realization theory of discrete-time linear switched systems
Automatica (Journal of IFAC)
Hi-index | 22.15 |
We propose a new framework for hybrid system identification, which relies on continuous optimization. This framework is based on the minimization of a cost function that can be chosen as either the minimum or the product of loss functions. The former is inspired by traditional estimation methods, while the latter is inspired by recent algebraic and support vector regression approaches to hybrid system identification. In both cases, the identification problem is recast as a continuous optimization program involving only the real parameters of the model as variables, thus avoiding the use of discrete optimization. This program can be solved efficiently by using standard optimization methods even for very large data sets. In addition, the proposed framework easily incorporates robustness to different kinds of outliers through the choice of the loss function.