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Neural network configurations for filtering of feed stream noise from oscillating continuous microbial fermentations

DIR@IMTECH: CSIR-Institute of Microbial Technology

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Title Neural network configurations for filtering of feed stream noise from oscillating continuous microbial fermentations
 
Creator Patnaik, P R
 
Subject QR Microbiology
 
Description Some microbial systems exhibit sustained oscillations under certain conditions.
The maintenance and the suppression of oscillations are both important in different
situations. While oscillations are clearly identifiable in small bioreactors, the influx of noise
fuzzifies the oscillations in larger vessels. So, noise-filtering devices are employed to recover
clear oscillating profiles. Recent work has shown that an auto-associative (AA) neural
network is a better than standard algorithmic filters. In this study, nine neural network
designs are compared for their ability to filter Gaussian noise in the substrate inflow rate of
a continuous fermentation containing Saccharomyces cerevisiae. While the AA network is
the best overall, specific performance criteria favor other designs. Thus the choice of a
neural filter depends on the evaluation criterion, which is guided by the application.
 
Publisher BAS, Centre of Biomedical Engineering
 
Date 2006-04-26
 
Type Article
PeerReviewed
 
Format application/pdf
 
Identifier http://crdd.osdd.net/open/1029/1/patnaik2006.4.pdf
Patnaik, P R (2006) Neural network configurations for filtering of feed stream noise from oscillating continuous microbial fermentations. Bioautomation, 4. pp. 45-56. ISSN 1312 – 451X
 
Relation http://www.clbme.bas.bg/bioautomation/2006/vol_4.1/files/4_3.3.pdf
http://crdd.osdd.net/open/1029/