Neural model of Cry1A(c) protein produced from a B.t. fed batch fermentation

Author

Barrera-Cortés, J. ; Baruch, I. ; Vázquez-Cervantes, V. ; Valdez-Castro, L.

Author_Institution

Dept. of Biotechnol. & Bioeng., CINVESTAV-IPN, Mexico City, Mexico

Volume

2

fYear

2001

fDate

2001

Firstpage

1302

Abstract

This paper presents a recurrent trainable neural network (RTNN) model of the fed-batch cultivation of Bacillus thuringiensis variety kurstaki HD-73 (B.t.). The proposed RTNN model has eleven inputs, six outputs and seventeen neurons in the hidden layer, with global and local feedback. The learning algorithm is a modified version of the backpropagation through time. The approximation error is below 2% and the generalization error is below 3%. The learning is performed in 51 epochs, 145 iterations each one. For the RTNN generalization, experimental data of one fermentation, not included in the learning process, are used

Keywords

backpropagation; biological specimen preparation; fermentation; generalisation (artificial intelligence); laboratory techniques; recurrent neural nets; Bacillus thuringiensis; backpropagation; fed batch fermentation; generalization; kurstaki HD-73; learning algorithm; trainable recurrent neural network; Backpropagation algorithms; Biomedical engineering; Biotechnology; Microorganisms; Neural networks; Output feedback; Proteins; Recurrent neural networks; Solids; Temperature control;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on

Conference_Location

Washington, DC

ISSN

1098-7576

Print_ISBN

0-7803-7044-9

Type

conf

DOI

10.1109/IJCNN.2001.939549

Filename

939549