Wavelet networks: an alternative to classical neural networks

Abstract

Artificial neural networks (ANNs) are being widely used to predict and forecast highly nonlinear systems. Recently, Wavelet networks (WNs) have been shown to be a promising alternative to traditional neural networks. In this study, the robustness of WNs and ANNs in modeling two distinct time series is investigated. The first series represents a chaotic system (Henon map) and the second series represents a stochastic geophysical time series (streamflows). Monthly streamflow values of the English river between Umferville and Sioux Lookout, ON, Canada, are considered in this study. For the implementation of traditional ANNs, the weights and bias values are optimized using genetic algorithms (GAs). However, in WNs, along with weights and bias, the translation and dilation factors of wavelets are also optimized. Use of GAs to optimize the network parameters is to overcome the problem of convergence towards local optima. Results from the study indicate that, WNs are more suitable for modeling short time high frequency time series like Henon map. However, performance of WNs is comparable with that of ANNs in modeling low frequency time series like streamflows.