Application of radial basis function and feedforward artificial neural networks to the Escherichia coli fermentation process

Radial basis function and feedforward neural networks are considered for modelling of the recombinant Escherichia coli fermentation process. The models use industrial on-line data from the process as input variables in order to estimate the concentrations of biomass and recombinant protein, normally only available from off-line laboratory analysis. The models performances are compared by prediction error and graphical fit using results obtained from a common testing set of fermentation data.