The nature of statistical learning theory
The nature of statistical learning theory
Piecewise linear solution paths with application to direct weight optimization
Automatica (Journal of IFAC)
Brief paper: Segmentation of ARX-models using sum-of-norms regularization
Automatica (Journal of IFAC)
Brief paper: A continuous optimization framework for hybrid system identification
Automatica (Journal of IFAC)
Identification of switched linear systems via sparse optimization
Automatica (Journal of IFAC)
Comparison of four procedures for the identification of hybrid systems
HSCC'05 Proceedings of the 8th international conference on Hybrid Systems: computation and control
Brief Equivalence of hybrid dynamical models
Automatica (Journal of IFAC)
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)
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Hinging hyperplanes for regression, classification, and function approximation
IEEE Transactions on Information Theory
| Hi-index | 22.14 |
This paper proposes a general convex framework for the identification of switched linear systems. The proposed framework uses over-parameterization to avoid solving the otherwise combinatorially forbidding identification problem, and takes the form of a least-squares problem with a sum-of-norms regularization, a generalization of the @?"1-regularization. The regularization constant regulates the complexity and is used to trade off the fit and the number of submodels.