Experimental Validation of Cascade Recurrent Neural Network Models

Abstract

This chapter examines cascade RNN models for modelling bench-scale fedbatch fermentation of Saccharomyces cerevisiae. The models are experimentally identified through training and validating using the data collected from experiments with different feed rate profiles. Data preprocessing methods are used to improve the robustness of the neural network models. The results show that the best biomass prediction ability is given by a DO cascade neural model.