Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction. Alarcon-Aquino, V. & Barria, J. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 36(2):208-220, 3, 2006. ![Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction [link]](https://bibbase.org/img/filetypes/link.svg "link")
Website doi abstract bibtex 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 translation-invariant 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 generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm. © 2006 IEEE.
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title = {Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction},
type = {article},
year = {2006},
keywords = {Finite-impulse-response (FIR) neural networks,Multiresolution learning,Network traffic prediction,Wavelet transforms,Wavelets},
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abstract = {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 translation-invariant 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 generalization ability of the FIR neural network is improved by the proposed multiresolution learning algorithm. © 2006 IEEE.},
bibtype = {article},
author = {Alarcon-Aquino, V. and Barria, J.A.},
doi = {10.1109/TSMCC.2004.843217},
journal = {IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews)},
number = {2}
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