EVOLUTIONARY FEED-FORWARD NEURAL NETWORKS FOR TRAFFIC PREDICTION
Mauro Annunziato, I. Bertini, Alessandro Pannicelli, Stefano Pizzuti · 2003
In this paper we show different evolutionary algorithms in order to optimise on-line weights of feed-forward neural networks when applied to short term (20 min.) urban traffic prediction. We compare the evolutionary methods with the classical back-propagation algorithm and we show results when weights are off-line and on-line evolved. Preliminary results are very promising and show the effectiveness of the proposed approach in order to get neural models capable to dynamically adapt to environmental changes.