%0 Journal Article
%T WAVELET-NETWORK BASED ON L1-NORM MINIMISATION FOR LEARNING CHAOTIC TIME SERIES
%A V. Alarcon-Aquino
%A E. S. Garcia-TreviŁżo
%A R. Rosas-Romero
%A J. F. Ramirez-Cruz
%J Journal of applied research and technology
%D 2005
%I Universidad Nacional Aut¨®noma de M¨¦xico (UNAM)
%X This paper presents a wavelet-neural network based on the L1-norm minimisation for learning chaotic time series.The proposed approach, which is based on multi-resolution analysis, uses wavelets as activation functions in thehidden layer of the wavelet-network. We propose using the L1-norm, as opposed to the L2-norm, due to the wellknownfact that the L1-norm is superior to the L2-norm criterion when the signal has heavy tailed distributions oroutliers. A comparison of the proposed approach with previous reported schemes using a time series benchmark ispresented. Simulation results show that the proposed wavelet-network based on the L1-norm performs better thanthe standard back-propagation network and the wavelet-network based on the traditional L2-norm when applied tosynthetic data.
%K Wavelet-networks
%K Wavelets
%K Multi-resolution Analysis
%K Learning Chaotic Time Series.
%U http://amcath.ccadet.unam.mx/jart/vol3_3/wavelet.pdf