A feedforward neural network for the wavelet decomposition of discrete time signals

摘要

A feedforward neural network with sigmoidal activation functions is proposed to perform the wavelet decomposition of a discrete time signals. The proposed network is made of two parts, the main network and the auxiliary network. The learning of the auxiliary network is achieved off-line, in a prior phase, in order to identify the desired wavelet. This identification is possible due to the properties of a neural network with one hidden layer to approximate any continuous function with a desired accuracy.