HYBRID NEURAL NETWORK MODELS OF BIOPROCESSES: A COMPARATIVE STUDY

摘要

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.