1996
DOI: 10.1016/0032-9592(95)00043-7
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On data-based modelling techniques for fermentation processes

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Cited by 54 publications
(21 citation statements)
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“…Especially, when the order of the polynomial is high and when there are many inputs, this complicates the selection of the appropriate terms in the polynomial. This was already pointed out by Warnes et al (1996). The computational speed of the fast noniterative training algorithm for neural networks was essential in the procedure to select the number of hidden nodes.…”
Section: Discussionmentioning
confidence: 93%
“…Especially, when the order of the polynomial is high and when there are many inputs, this complicates the selection of the appropriate terms in the polynomial. This was already pointed out by Warnes et al (1996). The computational speed of the fast noniterative training algorithm for neural networks was essential in the procedure to select the number of hidden nodes.…”
Section: Discussionmentioning
confidence: 93%
“…[ 21] The number of nodes in a hidden layer is determined by the user and can vary from zero to any finite number. The number of nodes in the input and output layers are determined by the number of inputs and by the output variables, respectively.…”
Section: Narx Neural Modelingmentioning
confidence: 99%
“…2c, BBM 1) the initial PenG concentration ([PenG] 0 ), the temperature (T k ), and the amount of added base (B k ) were taken as inputs. For (fed-)-batch processes often the time is given as input to black box models (Albiol et al, 1995;Montague and Morris, 1994;Warnes et al, 1996;Zhang et al, 1994). Therefore, for the second BBM (Fig.…”
Section: Simulation Study Setupmentioning
confidence: 99%