Nonlinear ill-posed problem analysis in model-based parameter estimation and experimental design
Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for...
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| Hauptverfasser: | , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
12 March 2015
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| In: |
Computers & chemical engineering
Year: 2015, Jahrgang: 77, Pages: 24-42 |
| ISSN: | 0098-1354 |
| DOI: | 10.1016/j.compchemeng.2015.03.002 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.compchemeng.2015.03.002 Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0098135415000733 
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| Verfasserangaben: | Diana C. López C., Tilman Barz, Stefan Körkel, Günter Wozny |
MARC
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| 520 | |a Discrete ill-posed problems are often encountered in engineering applications. Still, their sound analysis is not yet common practice and difficulties arising in the determination of uncertain parameters are typically not assigned properly. This contribution provides a tutorial review on methods for identifiability analysis, regularization techniques and optimal experimental design. A guideline for the analysis and classification of nonlinear ill-posed problems to detect practical identifiability problems is given. Techniques for the regularization of experimental design problems resulting from ill-posed parameter estimations are discussed. Applications are presented for three different case studies of increasing complexity. | ||
| 650 | 4 | |a Identifiability problems | |
| 650 | 4 | |a Ill-conditioning analysis | |
| 650 | 4 | |a Ill-posed problems | |
| 650 | 4 | |a Parameter subset selection | |
| 650 | 4 | |a Singular value decomposition | |
| 650 | 4 | |a Tikhonov regularization | |
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