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2016 | OriginalPaper | Chapter

Parameter Estimation Algorithms for Kinetic Modeling from Noisy Data

Authors : Fabiana Zama, Dario Frascari, Davide Pinelli, A. E. Molina Bacca

Published in: System Modeling and Optimization

Publisher: Springer International Publishing

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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.

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Metadata
Title
Parameter Estimation Algorithms for Kinetic Modeling from Noisy Data
Authors
Fabiana Zama
Dario Frascari
Davide Pinelli
A. E. Molina Bacca
Copyright Year
2016
DOI
https://doi.org/10.1007/978-3-319-55795-3_49

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