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 semiconvergent 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.

Zama, F., Frascari, D., Pinelli, D., Molina Bacca, A. (2016). Parameter estimation algorithms for kinetic modeling from noisy data. Berlin : Springer New York LLC [10.1007/978-3-319-55795-3_49].

Parameter estimation algorithms for kinetic modeling from noisy data

ZAMA, FABIANA;FRASCARI, DARIO;PINELLI, DAVIDE;MOLINA BACCA, AURORA ESTHER
2016

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 semiconvergent 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.
2016
IFIP Advances in Information and Communication Technology
517
527
Zama, F., Frascari, D., Pinelli, D., Molina Bacca, A. (2016). Parameter estimation algorithms for kinetic modeling from noisy data. Berlin : Springer New York LLC [10.1007/978-3-319-55795-3_49].
Zama, Fabiana; Frascari, Dario; Pinelli, Davide; Molina Bacca, A.E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/591956
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