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

  • Francisco Cruz
  • , Gonzalo Acuña
  • , Francisco Cubillos
  • , Vicente Moreno
  • , Danilo Bassi
  • Universidad de Santiago de Chile

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - ISNN 2007 - 4th International Symposium on Neural Networks, ISNN 2007, Proceedings
PublisherSpringer Verlag
Pages391-397
Number of pages7
EditionPART 2
ISBN (Print)9783540723929
DOIs
StatePublished - 2007
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4492 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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