In:
Advanced Materials Research, Trans Tech Publications, Ltd., Vol. 160-162 ( 2010-11), p. 1749-1755
Abstract:
To predict and control feed batch fermentations of Corynebacterium glutamicun TQ2226 which can produce L-histidine , in this paper , we use a recurrent neural network model(RNNM).The control variables are the limiting substrate and the feeding conditions. The multi-input and multi-output RNNM proposed has seven outputs, nineteen neurons, twelve inputs, in the hidden layer, and 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 RNNM generalization was carried out reproducing a C. glutamicum fermentation not included in the learning process. It attains an error approximation of 1.8%.
Type of Medium:
Online Resource
ISSN:
1662-8985
DOI:
10.4028/www.scientific.net/AMR.160-162
DOI:
10.4028/www.scientific.net/AMR.160-162.1749
Language:
Unknown
Publisher:
Trans Tech Publications, Ltd.
Publication Date:
2010
detail.hit.zdb_id:
2265002-7
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