WAVELET NEURAL NETWORKS FOR FUNCTION LEARNING

In this paper, a wavelet-based neural network is described. The struct ure of this network is similar to that of the radial basis function (R BP) network, except that here the radial basis functions are replaced by orthonormal scaling functions that are not necessarily radial symme tric. The efficacy of this type of network in function learning and es timation is demonstrated through theoretical analysis and experimental results. In particular, it has been shown that the wavelet network ha s universal and L(2) approximation properties and is a consistent func tion estimator. Convergence rates associated with these properties are obtained for certain function classes where the rates avoid the ''cur se of dimensionality.'' In the experiments, the wavelet network perfor med well and compared favorably to the MLP and RBF networks.