A Recurrent Neural Network for Controlling a Fed-Batch Fermentation of B. t.

  • J. Barrera Cortés
  • I. Baruch
  • L. Valdez Castro
  • V. Vázquez Cervantes
Conference paper


The paper proposed to use a new Recurrent Neural Network Model (RNNM) to stabilize fermentation process of Bacillus thuringiensis from fermentation kinetic data. The multi-input multi-output RNNM proposed, have ten inputs, six outputs, sixteen neurones in the hidden layer, and also global and local feedbacks. The weight update learning algorithm designed, is a version of the well known backpropagation through time algorithm, directed to the RNNM learning. The approximation error for the last epoch of learning is about 2% and the total time of learning is 201 epochs, where the size of epoch is 115 iterations.


Hide Layer Mean Square Error Specific Growth Rate Bacillus Thuringiensis Recurrent Neural Network 


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Copyright information

© Springer-Verlag Wien 2001

Authors and Affiliations

  • J. Barrera Cortés
  • I. Baruch
  • L. Valdez Castro
  • V. Vázquez Cervantes
    • 1
  1. 1.CINVESTAV-IPNMéxico D. F.México

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