Prediction of Network Traffic Using Multiscale-Bilinear Recurrent Neural Network with Adaptive Learning

  • Dong-Chul Park
Part of the Communications in Computer and Information Science book series (CCIS, volume 15)


A prediction scheme for network traffics using Multiscale-Bilinear Recurrent Neural Network (M-BLRNN) with adaptive learning procedure is proposed and presented in this paper. The proposed predictor is a combination between M-BLRNN and adaptive learning procedure. In M-BLRNN, the wavelet transform is employed to decompose the original traffic signals into several simple traffic signals. In addition, the adaptive learning procedure is applied to improve the learning process at each resolution level in M-BLRNN with adaptive learning (M-BLRNN(AL)). Experiments and results on a Ethernet network traffic prediction problem show that the proposed M-BLRNN(AL) scheme converges faster than M-BLRNN. The prediction accuracies of M-BLRNN and M-BLRNN(AL) are very similar in terms of the normalized mean square error(NMSE).


prediction time-series recurrent neural network 


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  1. 1.
    Wu, W.R., Chen, P.C.: Adaptive AR Modeling in White Gaussian Noise. IEEE Trans. on Signal Processing 45, 1184–1192 (1997)CrossRefGoogle Scholar
  2. 2.
    Kiruluta, A., Eizenman, M., Pasupathy, S.: Predictive Head Movement Tracking using a Kalman Filter. IEEE Trans. on Systems, Man and Cybernetics 27, 326–331 (1997)CrossRefGoogle Scholar
  3. 3.
    Chun, Y., Chandra, K.: Time series models for the internet data traffic. In: 24th Conference on the Local Computer Networks, pp. 164–171 (1999)Google Scholar
  4. 4.
    Aimin, S., Sanqi, Li.: A predictability analysis of the network traffic. INFOCOM 1, 342–351 (2000)Google Scholar
  5. 5.
    Park, D.C., El-Sharkawi, M.A., Marks II, R.J., Atlas, L.E., Damborg, M.J.: Electronic Load Forecasting using an Artificial Neural Network. IEEE Trans. Power System 6, 442–449 (1991)CrossRefGoogle Scholar
  6. 6.
    Leung, H., Lo, T., Wang, S.: Prediction of Noisy Chaotic Time Series using an Optimal Radial Basis Function Neural Network. IEEE Trans. on Neural Networks 12, 1163–1172 (2001)CrossRefGoogle Scholar
  7. 7.
    Park, D.C., Tran, C.N., Lee, Y.: Multiscale BiLinear Recurrent Neural Networks and Their Application to the Long-Term Prediction of Network Traffic. In: Wang, J., Yi, Z., Żurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 196–201. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Park, D.C., Jeong, T.K.: Complex Bilinear Recurrent Neural Network for Equalization of a Satellite Channel. IEEE Trans. on Neural Network 13, 711–725 (2002)CrossRefGoogle Scholar
  9. 9.
    Mallat, S.G.: A Theory for Multiresolution Signal Decomposition: the Wavelet Representation. IEEE Trans. on Pattern Analysis and Machine Intelligence 11, 674–693 (1989)MATHCrossRefGoogle Scholar
  10. 10.
    Liang, Y., Page, E.W.: Multi resolution Learning Paradigm and Signal Prediction. IEEE Trans. Sig. Proc. 45, 2858–2864 (1997)CrossRefGoogle Scholar
  11. 11.
    Renaud, O., Starck, J.L., Murtagh, F.: Wavelet-Based Combined Signal Filtering and Prediction. IEEE Trans. on Systems, Man and Cybernetics 35, 1241–1251 (2005)CrossRefGoogle Scholar
  12. 12.
    Fowler, H.J., Leland, W.E.: Local Area Network Traffic Characteristics with Implications for Broadband Network Congestion Management. In: IEEE JSAC, pp. 1139–1149 (1991)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Dong-Chul Park
    • 1
  1. 1.Center for Intelligent Imaging Systems Research, Dept. of Information EngineeringMyong Ji UniversityKorea

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