Learning and Approximation of Chaotic Time Series Using Wavelet-Networks

Authors:
V. Alarcon-Aquino;E. S. Garcia-Trevino;R. Rosas-Romero;J. F. Ramirez-Cruz
Affiliations:
Universidad de las Americas-Puebla, Mexico;Universidad de las Americas-Puebla, Mexico;Universidad de las Americas-Puebla, Mexico;Instituto Technologico de Apizaco, Mexico
Venue:
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Year:
2005

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Abstract

This paper presents a wavelet neural-network for learning and approximation pf chaotic time series. Wavelet networks are a class of neural network that take advantage of good localization and approximation properties of multiresolution analysis. These networks use wavelets as activation fynction in the hidden layer and a hierarchical method is used for learning. Comparisons are made between a wavelet network tested with two different wavelets, and the typical feedforward network trained with the back-propagation algorithm. The results reported in this paper show that wavelet networks have better approximation properties than back-propagation networks.