A NEURAL-NETWORK ARCHITECTURE FOR NOISE PREDICTION

This paper presents application of neural networks (NNs) to the problem of prediction of noise caused by urban traffic. The most representative physical variable quantifying noise emissions ir the equivalent sound pressure level Up to now it has been identified on the basis of semi-empirical models, typically regression analysis, which generally do not provide very accurate approximations of the trend followed by sound pressure level. The authors have attempted to overcome this difficulty by adopting a neural approach based on a BackPropagation Network (BPN). Results obtained by the comparison of the BPN approach with those provided by selected relationships found in relevant literature, show how good is the approach proposed. The neural solution to the problem has shown the necessity, in certain phases, of a set of acoustic measurements which is as free as possible of error. The complexity of error identification by means of classical approaches has led the authors to explore the possibility of a neural solution to this problem as well. The authors therefore propose use of a neural architecture made up of two cascading levels. At the first level a supervised classifying network, the learning vector quantization (LVQ) network,filters the data discarding all the wrong measurements, while at the second level the BPN predicts the sound pressure level.