Abstract:
:This work proposes a committee of Cascade Correlation neural networks as an online smoother for random measurement noise. The goals of this paper are twofold: first it intends to explore the possibilities of using a constructive neural network algorithm to learn how to filter typical noisy data from a bioreactor, CO(2) mol fractions of the effluent gas during the aerobic cultivation of Bacillus megaterium to produce the enzyme penicillin G acylase. Second, to propose a committee of such networks for achieving more realistic results, capturing the inherent trend of the process. In order to do that this paper discusses the advantages of using a constructive neural network algorithm, describes how the committee of NNs operates and evaluates its performance using real CO(2) online data obtained in laboratorial experiments. The paper also presents results obtained with classical filtering algorithms, for comparison.
journal_name
Bioprocess Biosyst Engjournal_title
Bioprocess and biosystems engineeringauthors
Giordano RC,Bertini JR Jr,Nicoletti MC,Giordano RLdoi
10.1007/s00449-007-0152-xsubject
Has Abstractpub_date
2008-02-01 00:00:00pages
101-9issue
2eissn
1615-7591issn
1615-7605journal_volume
31pub_type
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