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Indirect training of grey-box models: application to a bioprocess

conference contribution
posted on 01.01.2007, 00:00 authored by Francisco CruzFrancisco Cruz, G Acuña, F Cubillos, V Moreno, D Bassi
Grey-box neural models mix differential equations, which act as white boxes, and neural networks, used as black boxes. The purpose of the present work is to show the training of a grey-box model by means of indirect backpropagation and Levenberg-Marquardt in Matlab®, extending the black box neural model in order to fit the discretized equations of the phenomenological model. The obtained grey-box model is tested as an estimator of a state variable of a biotechnological batch fermentation process on solid substrate, with good results. © Springer-Verlag Berlin Heidelberg 2007.

History

Volume

4492 LNCS

Issue

PART 2

Pagination

391 - 397

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783540723929

Publication classification

E1.1 Full written paper - refereed

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)