Knowledge and Information Systems

, Volume 19, Issue 2, pp 235–248 | Cite as

Multiresolution-based bilinear recurrent neural network

  • Dong-Chul ParkEmail author
Regular Paper


A multiresolution-based bilinear recurrent neural network (MBLRNN) is proposed in this paper. The proposed MBLRNN is based on the BLRNN that has robust abilities in modeling and predicting time series. The learning process is further improved by using a multiresolution-based learning algorithm for training the BLRNN so as to make it more robust for the prediction of time series data. The proposed MBLRNN is applied to the problems of network traffic prediction and electric load forecasting. Experiments and results on both practical problems show that the proposed MBLRNN outperforms both the traditional multilayer perceptron type neural network (MLPNN) and the BLRNN in the prediction accuracy.


Wavelet transform Recurrent neural network Time series prediction Network traffic Load forecasting 


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  1. 1.
    Alarcon-Aquino V, Barria JA (2005) Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction. IEEE Trans Syst Man Cyber PP 99: 1–13Google Scholar
  2. 2.
    Aussem A, Murtagh F (1998) A neuro-wavelet strategy for web traffic forecasting. J Offic Stat 1: 65–87Google Scholar
  3. 3.
    Broersen PMT (2002) Automatic spectral analysis with time series models. IEEE Trans Instrum Meas 51(2): 211–216CrossRefMathSciNetGoogle Scholar
  4. 4.
    Chiang TW, Tsai T (2008) Querying color images using user-specified wavelet features. Knowl Inf Sys (KAIS) 15(1): 109–129CrossRefMathSciNetGoogle Scholar
  5. 5.
    Connor JT, Martin RD, Atlas LE (1994) Recurrent neural networks and robust time series prediction. IEEE Trans Neural Netw 5(2): 240–254CrossRefGoogle Scholar
  6. 6.
    Garfield S, Wermter S (2006) Call classification using recurrent neural networks, support vector machines and finite state automata. Knowl Inf Sys (KAIS) 9(2): 131–156CrossRefGoogle Scholar
  7. 7.
    Geva AB (1998) ScaleNet-multiscale neural-network architecture for time series prediction. IEEE Trans Neural Netw 9(6): 1471–1482CrossRefGoogle Scholar
  8. 8.
    He Z, Wang XS, Lee BS, Ling ACH (2008) Mining partial periodic correlation in time series. Knowl Inf Sys (KAIS) 15(1): 31–54CrossRefGoogle Scholar
  9. 9.
    Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: a review and evaluation. IEEE Trans Power Syst 16: 44–55CrossRefGoogle Scholar
  10. 10.
    Kiruluta A, Eizenman M, Pasupathy S (1997) Predictive head movement tracking using a Kalman filter. IEEE Trans Syst Man Cyber 27(2): 326–331CrossRefGoogle Scholar
  11. 11.
    Leland WE, Wilson DV (1991) High time-resolution measurement and analysis of LAN traffic: implications for LAN interconnection. In: Proceedings of IEEE INFOCOM, pp 1360–1366Google Scholar
  12. 12.
    Leung H, Lo T, Wang S (2001) Prediction of noisy chaotic time series using an optimal radial basis function neural network. IEEE Trans Neural Netw 12(5): 1163–1172CrossRefGoogle Scholar
  13. 13.
    Liang Y, Page EW (1997) Multiresolution learning paradigm and signal prediction. IEEE Trans Signal Process 45: 2858–2864CrossRefGoogle Scholar
  14. 14.
    Mallat SG (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11: 674–693zbMATHCrossRefGoogle Scholar
  15. 15.
    Marsolo K, Parthasarathy S (2007) On the use of structure and sequence-based features for protein classification and retrieval. Knowl Inf Sys (KAIS) 14(1): 59–80CrossRefGoogle Scholar
  16. 16.
    Park DC, El-Sharkawi MA, Marks RJ II, Atlas LE, Damborg MJ (2001) Electric load forecasting using an artificial neural network. IEEE Trans Power Syst 6: 442–449CrossRefGoogle Scholar
  17. 17.
    Park DC, Jeong TK (2002) Complex bilinear recurrent neural network for equalization of a satellite channel. IEEE Trans Neural Netw 13: 711–725CrossRefGoogle Scholar
  18. 18.
    Park DC, Zhu Y (1994) Bilinear recurrent neural network. In: Proceedings of IEEE International Conference on Neural Networks 3, pp 1459–1464Google Scholar
  19. 19.
    Renaud O, Starck JL, Murtagh F (2005) Wavelet-based combined signal filtering and prediction. IEEE Trans Syst Man Cyber 35: 1241–1251CrossRefGoogle Scholar
  20. 20.
    Shensa MJ (1992) The discrete wavelet transform: wedding the À Trous and Mallat algorithms. IEEE Trans Signal Proc 10: 2463–2482Google Scholar
  21. 21.
    Stamatis N, Parthimos D, Griffith TM (1999) Forecasting Chaotic cardiovascular time series with an adaptive slope multilayer perceptron neural network. IEEE Trans Biomed Eng 46(12): 1441–1453CrossRefGoogle Scholar
  22. 22.
    Wu WR, Chen PC (1997) Adaptive AR modeling in white Gaussian noise. IEEE Trans Signal Process 45(5): 1184–1192CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  1. 1.ICRL, Department of Information EngineeringMyong Ji UniversityYong InRepublic of Korea

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