Adaptive Neural Models for Online Prediction in Fermentation

Abstract

This paper deals with on-line prediction of fermentation variables by neural network techniques. It is shown that the accuracy of the on-line prediction based on a neural model, obtained from an initial learning sequence, decreases when kinetic changes occur during the course of the fermentation. Therefore, sliding window learning schemes are proposed. For a given network structure, the proposed learning procedures progressively refresh the knowledge integrated within an initial neural model. The influence of the length of the learning window, the number of iterations and the initial neural model on the predictive accuracy of adaptive neural models are investigated. Sliding window learning schemes can be also used when fermentation measurements are delayed and/or infrequent