Robust identification of switched regression models
| Autor: | Didier Maquin, Elom Ayih Domlan, José Ragot, Biao Huang |
|---|---|
| Přispěvatelé: | Department of Chemical and Materials Engineering, University of Alberta, Centre de Recherche en Automatique de Nancy (CRAN), Université Henri Poincaré - Nancy 1 (UHP)-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2009 |
| Předmět: |
0209 industrial biotechnology
Control and Optimization 02 engineering and technology Machine learning computer.software_genre [SPI.AUTO]Engineering Sciences [physics]/Automatic 020901 industrial engineering & automation Robustness (computer science) Control theory 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Mathematics Estimation theory business.industry System identification Regression analysis Computer Science Applications Human-Computer Interaction Parameter identification problem Data point Control and Systems Engineering Outlier A priori and a posteriori 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer |
| Zdroj: | IET Control Theory and Applications IET Control Theory and Applications, Institution of Engineering and Technology, 2009, 3 (12), pp.1578-1590. ⟨10.1049/iet-cta.2008.0274⟩ |
| ISSN: | 1751-8652 1751-8644 |
| DOI: | 10.1049/iet-cta.2008.0274 |
| Popis: | International audience; This study addresses the problem of parameters estimation for switched regression models used to represent systems with multiple operating modes or regimes. For the identification of such models, the collected data are from different operating modes and there is no a priori information holding on the partitioning of the data in regard to the different operating modes. The essential contributions of this paper lie first in the estimation procedure of the model parameters that provides an analytical solution, second in the simultaneous resolution of the problem of estimating the model parameters and allocating the data points to the different local models, and finally the robustness of the estimation procedure regarding the presence of outliers in the identification dataset. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|