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Multiresolution FIR Neural Network Based Learning Algorithm Applied to Network Traffic Prediction

V. Alarco-Aquino and J. A. Barria

01/03/2006

In this paper a multiresolution finite impulse response (FIR) neural network based learning algorithm using the maximal overlap discrete wavelet transform (MODWT) is proposed. The multiresolution learning algorithm employs the analysis framework of wavelet theory, which decomposes a signal into wavelet coefficients and scaling coefficients. The translationinvariant property of the MODWT allows aligment of events in a multiresolution analysis with respect to the original time series, and therefore preserving the integrity of some transient events. A learning algorithm is also derived for adapting the gain of the activation functions at each level of resolution. The proposed multiresolution FIR neural network based learning algorithm is applied to network traffic prediction (real world aggregate Ethernet traffic data) with comparable results. These results indicate that the generalisation ability of the FIR neural network is improved by the proposed multiresolution learning algorithm.