Parameter Estimation Algorithms for Kinetic Modeling from Noisy Data

Abstract : The aim of this work is to test the Levemberg Marquardt and BFGS (Broyden Fletcher Goldfarb Shanno) algorithms, implemented by the matlab functions lsqnonlin and fminunc of the Optimization Toolbox, for modeling the kinetic terms occurring in chemical processes of adsorption. We are interested in tests with noisy data that are obtained by adding Gaussian random noise to the solution of a model with known parameters. While both methods are very precise with noiseless data, by adding noise the quality of the results is greatly worsened. The semi-convergent behaviour of the relative error curves is observed for both methods. Therefore a stopping criterion, based on the Discrepancy Principle is proposed and tested. Great improvement is obtained for both methods, making it possible to compute stable solutions also for noisy data.
Type de document :
Communication dans un congrès
Lorena Bociu; Jean-Antoine Désidéri; Abderrahmane Habbal. 27th IFIP Conference on System Modeling and Optimization (CSMO), Jun 2015, Sophia Antipolis, France. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-494, pp.517-527, 2016, System Modeling and Optimization. 〈10.1007/978-3-319-55795-3_49〉
Liste complète des métadonnées

Littérature citée [13 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01626909
Contributeur : Hal Ifip <>
Soumis le : mardi 31 octobre 2017 - 14:41:10
Dernière modification le : mardi 31 octobre 2017 - 14:44:55
Document(s) archivé(s) le : jeudi 1 février 2018 - 13:12:58

Fichier

 Accès restreint
Fichier visible le : 2019-01-01

Connectez-vous pour demander l'accès au fichier

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Fabiana Zama, Dario Frascari, Davide Pinelli, A. Bacca. Parameter Estimation Algorithms for Kinetic Modeling from Noisy Data. Lorena Bociu; Jean-Antoine Désidéri; Abderrahmane Habbal. 27th IFIP Conference on System Modeling and Optimization (CSMO), Jun 2015, Sophia Antipolis, France. Springer International Publishing, IFIP Advances in Information and Communication Technology, AICT-494, pp.517-527, 2016, System Modeling and Optimization. 〈10.1007/978-3-319-55795-3_49〉. 〈hal-01626909〉

Partager

Métriques

Consultations de la notice

27