Using radial basis function networks for classification problems

Multi-layer perceptron is now widely used in classification problems, whereas radial basis function networks (RBFNs) appear to be rather less well known. Purpose of this work is to briefly recall RBFNs and to allow a synthesis of theirs best features. The relationships between these networks and other well-developed methodological tools for classification, both in neural computing and in statistics, are shown. The application of these networks to the forensic glass data set, which is not new in literature, try to lay out what is common and what is distinctive in these networks and other competitive methods and to show, through empirical validation, the networks performance.