Hybrid Neural Network Models of Bioprocesses: A Comparative Study
| Authors: | Grosfils Aline, Université Libre de Bruxelles, Belgium
Vande Wouwer Alain, Faculté Polytechnique de Mons, Belgium
Bogaerts Philippe, Université Libre de Bruxelles, Belgium |
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| Topic: | 8.4 Control of Biotechnological Processes |
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| Session: | Control of Biotechnological Processes |
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| Keywords: | biotechnology, reactor modeling, mathematical models, neural network, maximum likelihood estimator |
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Abstract
Modeling of bioprocesses for engineering applications is a very difficult and time consuming task, due to their complex nonlinear dynamic behaviour. In the last years several propositions for hybrid models were published and discussed, in order to combine analytical prior knowledge with the learning capabilities of neural networks. This paper proposes a comparison between several hybrid models based on the two most widespread neural networks, the MultiLayer Perceptron and the Radial Basis Function network. This evaluation relies on simulations of fed-batch bacterial cultures.
