Detalle de: Robust Identification in Nonlinear Dynamic Process Models
| Title | Robust Identification in Nonlinear Dynamic Process Models |
| Abstract |
Parameter estimation is a key issue in the mathematical modelling of nonlinear dynamic processes. Standard (gradient-based) methods for data fitting in nonlinear dynamic systems can suffer from slow and/or local convergence, among other problems. However, this is frequently ignored, potentially leading to wrong conclusions about the validity of a model regarding a certain data set. In order to surmount these difficulties, we present alternative methods based on global optimisation and identifiability analysis. |
| Apa Reference | Rodríez‐Fernáez, M., Alonso, A. A., & Banga, J. R. (2013). Robust Identification in Nonlinear Dynamic Process Models. Taming Heterogeneity and Complexity of Embedded Control, 635–644. Portico. 10.1002/9780470612217.ch35 |
| Doi | 10.1002/9780470612217.ch35 |
| Publication Date | Mar 2013 |
| Publication Type | Other |
| Researcher | Julio Rodriguez Banga |
| Ontology | Biosystems Modeling |
| Research Groups | Computational Biology Lab at MBG |
| Tools |