On Comparison Between Radial Basis Function and Wavelet Basis Functions Neural Networks

In this paper we study and design two feed forward neural networks. The first approach uses radial basis function network and second approach uses wavelet basis function network to approximate the mapping from the input to the outp ut sp ace. The trained networks are then used in an conjugate gradient algorithm to estimate the outp ut. These neural networks are then applied to solve differential equation. Results of applying these algorithms to several examples are presented.