A retraining neural network technique for glass manufacturing data forecasting

This paper advances a retraining-neural-network-based forecasting mechanism that can be applied to complex prediction problems, such as the estimation of relevant process variables for glass manufacturing. The main purpose is to obtain a good accuracy of the predicted data by using an optimal feedforward neural architecture and well-suited delay vectors. The artificial neural network´s (ANNs) ability to extract significant information provides a valuable framework for the representation of relationships present in the structure of the data. The evaluation of the output error after the retraining of an ANN shows that the retraining technique can substantially improve the achieved results.