A comparison between weighted radial basis functions and wavelet networks

In the present paper, Wavelet Networks, are proven to be, as well as many other neural paradigms, a speci c case of the generic paradigm named Weighted Radial Basis Functions Networks. Moreover, a fair comparison between Wavelet and more traditional WRBF networks for function approximation is attempted, in order to demonstrate that the performance depends only on how good the chosen mother/activation function \ ts" the function itself.