%0 Journal Article
%@ 0951-208X
%A Patnaik, P R
%D 1995
%F open:698
%I Springer Science
%J Biotechnology Techniques
%N 9
%P 691-696
%T An evaluation of a neural network for the start-up phase of a continuous recombinant fermentation subject to disturbances
%U http://crdd.osdd.net/open/698/
%V 9
%X A radial basis neural network was applied to a process for glyceraldehyde-3-phosphate dehydrogenase produced by an Escherichia coli strain containing the plasmid pBR Eco gap. A neural network trained with a pure culture predicted the performance of a fermentation containing wild type cells and/or product in the inoculum better than in the reverse case; this is explained. In general, the network learnt the trends in the concentrations of plasmid-containing cells and the recombinant product more accurately than those of wild type cells and the substrate. This similarity with deterministic networks and the good predictability with some training vectors suggests that neural networks can be used to simulate the start-up phase of recombinant fermentations corrupted by disturbances.  Fulltext Preview 
%Z Copyright of this article belongs to Springer Science.