Chaotic Time Series Approximation Using Iterative Wavelet-Networks

Abstract

This paper presents a wavelet neural-network for learning and approximation of chaotic time series. Wavelet-networks are inspired by both feed-forward neural networks and the theory underlying wavelet decompositions. Wavelet networks a class of neural network that take advantage of good localization properties of multiresolution analysis and combine them with the approximation abilities of neural networks.. This kind of network uses wavelets as activation functions in the hidden layer and a type of backpropagation algorithm is used for its learning. Comparisons are made between a wavelet-network and the typical feed-forward networks trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than its similar backpropagation networks.