Wavelet Network for Recursive Function Learning

摘要

We present a wavelet neural network for recovering non-linear functions from random data. The network has modular architecture and exploits a father wavelet as activation function. Synaptic weights of the net are trained according to simple recursive rules and yield consistent estimates of function values on a pre-defined grid of points. The weights are then applied for reconstruction of the underlying non-linearity.