Documents disponibles écrits par cet auteur

Parameter set selection via clustering of parameters into pairwise indistinguishable groups of parameters / Yunfei Chu in Industrial & engineering chemistry research, Vol. 48 N° 13 (Juillet 2009) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6000–6009 Titre : Parameter set selection via clustering of parameters into pairwise indistinguishable groups of parameters Type de document : texte imprimé Auteurs : Yunfei Chu, Auteur ; Juergen Hahn, Auteur Année de publication : 2009 Article en page(s) : pp. 6000–6009 Note générale : Chemical engineering Langues : Anglais (eng) Mots-clés : Parameter  set  Clustering  Computational  effort  grows  drastically Résumé : Selecting a set of parameters to be estimated from experimental data is an important problem with many different types of applications. However, the computational effort grows drastically with the number of parameters in the model. This paper proposes a technique that reduces the parameters that need to be considered by clustering, where the model parameters are put into different groups on the basis of the dynamic effect that changes have on the model output. The computational requirements of the parameter set selection problem then drastically reduces as only one parameter per cluster needs to be considered instead of each parameter in the model. This paper develops the underlying theory of the presented technique and also illustrates the method on a model of a signal transduction pathway with 115 parameters. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800432s [article] Parameter set selection via clustering of parameters into pairwise indistinguishable groups of parameters [texte imprimé] / Yunfei Chu, Auteur ; Juergen Hahn, Auteur . - 2009 . - pp. 6000–6009. Chemical engineering Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 48 N° 13 (Juillet 2009) . - pp. 6000–6009 Mots-clés : Parameter  set  Clustering  Computational  effort  grows  drastically Résumé : Selecting a set of parameters to be estimated from experimental data is an important problem with many different types of applications. However, the computational effort grows drastically with the number of parameters in the model. This paper proposes a technique that reduces the parameters that need to be considered by clustering, where the model parameters are put into different groups on the basis of the dynamic effect that changes have on the model output. The computational requirements of the parameter set selection problem then drastically reduces as only one parameter per cluster needs to be considered instead of each parameter in the model. This paper develops the underlying theory of the presented technique and also illustrates the method on a model of a signal transduction pathway with 115 parameters. En ligne : http://pubs.acs.org/doi/abs/10.1021/ie800432s

Quantitative optimal experimental design using global sensitivity analysis via quasi-linearization / Yunfei Chu in Industrial & engineering chemistry research, Vol. 49 N° 17 (Septembre 1, 2010) 

Public ISBD [article]  in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7782–7794 Titre : Quantitative optimal experimental design using global sensitivity analysis via quasi-linearization Type de document : texte imprimé Auteurs : Yunfei Chu, Auteur ; Juergen Hahn, Auteur Année de publication : 2010 Article en page(s) : pp 7782–7794 Note générale : Chimie industrielle Langues : Anglais (eng) Mots-clés : Quantitative  optimal  Analysis. Résumé : Local sensitivity analysis is widely used in experimental design to improve the precision of the estimated parameters. However, for nonlinear models, the local sensitivity values and the experimental design criteria are dependent on the, not yet known, parameter values. Global sensitivity analysis can deal with this situation by taking parameter uncertainty into account for computation of the sensitivity values. However, the existing experimental design criteria cannot easily be applied to the conventional global sensitivity analysis results. One outcome of this is that experimental design involving global sensitivity analysis has mainly focused on identification of influential parameters. A new global sensitivity analysis technique is presented in this work for the purpose of using this technique for quantitative experimental design. The methodology makes use of quasi-linearization and the global sensitivity matrix returned is the design matrix of the linearized model. Due to this, the same experimental criteria that have been developed for quantitative optimal design of linear models can be applied and serve as indicators of desired properties of the parameter estimates. The presented design using global sensitivity analysis is consistent with the popular design involving local sensitivity analysis when the parameter uncertainty is small; however, the technique outperforms local design when applied to models with significant parameter uncertainty. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9009827 [article] Quantitative optimal experimental design using global sensitivity analysis via quasi-linearization [texte imprimé] / Yunfei Chu, Auteur ; Juergen Hahn, Auteur . - 2010 . - pp 7782–7794. Chimie industrielle Langues : Anglais (eng) in Industrial & engineering chemistry research > Vol. 49 N° 17 (Septembre 1, 2010) . - pp 7782–7794 Mots-clés : Quantitative  optimal  Analysis. Résumé : Local sensitivity analysis is widely used in experimental design to improve the precision of the estimated parameters. However, for nonlinear models, the local sensitivity values and the experimental design criteria are dependent on the, not yet known, parameter values. Global sensitivity analysis can deal with this situation by taking parameter uncertainty into account for computation of the sensitivity values. However, the existing experimental design criteria cannot easily be applied to the conventional global sensitivity analysis results. One outcome of this is that experimental design involving global sensitivity analysis has mainly focused on identification of influential parameters. A new global sensitivity analysis technique is presented in this work for the purpose of using this technique for quantitative experimental design. The methodology makes use of quasi-linearization and the global sensitivity matrix returned is the design matrix of the linearized model. Due to this, the same experimental criteria that have been developed for quantitative optimal design of linear models can be applied and serve as indicators of desired properties of the parameter estimates. The presented design using global sensitivity analysis is consistent with the popular design involving local sensitivity analysis when the parameter uncertainty is small; however, the technique outperforms local design when applied to models with significant parameter uncertainty. DEWEY : 660 ISSN : 0888-5885 En ligne : http://pubs.acs.org/doi/abs/10.1021/ie9009827