Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction

IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews

Volumn 36, Issue 2, 2006, Pages 208-220

  Alarcon Aquino, Vicente ^a   Barria, Javier A ^b  

^a UNIVERSIDAD DE LAS AMÉRICAS PUEBLA   (Mexico)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Finite impulse response (FIR) neural networks; Multiresolution learning; Network traffic prediction; Wavelet transforms; Wavelets

Indexed keywords

FORECASTING; LEARNING ALGORITHMS; TELECOMMUNICATION TRAFFIC; TIME SERIES ANALYSIS; WAVELET TRANSFORMS;

FINITE-IMPULSE-RESPONSE NEURAL NETWORKS; MULTIRESOLUTION LEARNING; NETWORK TRAFFIC PREDICTION;

NEURAL NETWORKS;

EID: 33646007938     PISSN: 10946977     EISSN: None     Source Type: Journal    
DOI: 10.1109/TSMCC.2004.843217     Document Type: Article

References (38)

1. A. D. Back and A. C. Tsoi, "FIR and IIR synapses, a new neural network architecture for time series modeling," Neural Comput., vol. 3, pp. 375-385, 1991.
2. B. R. Bakshi, "Multiscale analysis and modeling using wavelets," J. Chemometrics, vol. 13, pp. 415-434, 1999.
3. A. N. Birkett and R. A. Gouban, "Nonlinear adaptive filtering with FIR synapses and adaptive activation functions," in Proc. IEEE Int. Conf. Acoustics, Speech, Signal Processing, 1997, pp. 3321-3324.
4. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day, 1976.
5. L. Cao, "Support vector machines experts for time series forecasting," Neurocomput., vol. 51, pp. 321-339, 2003.
6. I. Daubechies, Ten Lectures on Wavelets. New York: SIAM, 1992.
7. M. Hallas and G. Dorffner, "A comparative study of feedforward and recurrent neural networks in time series prediction," in Proc. 14th European Meet. Cybernetics Systems Research, vol. 2, 1998, pp. 644-647.
8. S. Haykin, Adaptive Filter Theory, 3rd ed. Upper Saddle River, NJ: Prentice-Hall, 1996.
9. S. Haykin, "Neural networks expand SP's horizons," IEEE Signal Process. Mag., vol. 13, no. 2, pp. 24-49, Mar. 1996.
10. S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed. Upper Saddle River, NJ: Prentice-Hall, 1999.
11. T. Koskela, M. Lehtokangas, J. Saarinen, and K. Kaski, "Time series prediction with multilayer perceptron, FIR and elman neural network," in Proc. World Congr. Neural Network, 1996, pp. 491-496.
12. J. K. Kruschke and J. R. Movellan, "Benefits of gain: Speeded learning and minimal hidden layers in back-propagation networks," IEEE Trans. Syst., Man Cybern., vol. 21, no. 1, pp. 273-280, Jan./Feb. 1991.
13. A. Lapedes and R. Farber, "Non-Linear Signal Processing Using Neural Networks: Prediction and System Modeling," Los Alamos National Lab., Tech. Rep. LA-UR-87-2662, Theoretical Div., Los Alamos, NM, 1987.
14. S. Lawrence, C. Giles, and A. C. Tsoi, "What size neural network gives optimal generalization? convergence properties of backpropagation," Inst. Advanced Comput. Studies, Univ. Maryland, College Park, MD, Tech. Rep. UMIACS-TR-96-22 and CS-TR-3617, 1996.
15. W. E. Leland, M. S. Taqqu, W. Willinger, and D. V. Wilson, "On the self-similar nature of ethernet traffic (extended version)," IEEE/ACM Trans. Netw., vol. 2, no. 1, pp. 1-15, Feb. 1994.
16. Y. Liang and E. W. Page, "Multiresolution learning paradigm and signal prediction," IEEE Trans. Signal Process., vol. 45, no. 11, pp. 2858-2864, Nov. 1997.
17. Y. Liang, "Adaptive neural activation function in multiresolution learning," in Proc. IEEE Int. Conf. Systems, Man Cybernetics, vol. 4, 2000, pp. 2601-2606.
18. S. Mallat, A Wavelet Tour of Signal Processing, 2nd ed. New York: Academic, 1999.
19. D. Mandic and J. A. Chambers, Recurrent Neural Networks for Prediction.. New York: Wiley, 2001.
20. O. Nerrand, P. Roussel-Ragot, L. Personnaz, and G. Dreyfus, "Neural networks and nonlinear adaptive filtering: Unifying concepts and new algorithms," Neural Comput., vol. 5, pp. 165-199, 1993.
21. D. B. Percival an dA. T. Walden, Wavelet Methods for Time Series Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2000.
22. A. Sang and L. A. San-qi, "Predictability analysis of network traffic," in Proc. IEEE INFOCOM, 2000, pp. 342-351.
23. S. Soltani, "On the use of the wavelet decomposition for time series prediction," Neurocomput., vol. 48, no. 1-4, pp. 267-277, 2002.
24. G. Thimm, P. Moerland, and E. Fiesler, "The interchangeability of learning rate and gain in back-propagation neural networks," Neural Comput., vol. 8, no. 2, pp. 451-460, 1996.
25. E. A. Wan, "Finite impulse response neural networks with applications in time series prediction," Ph.D. dissertation, Dept. Elect. Eng., Stanford Univ., Stanford, CA, 1993.
26. A. S. Weigend and N. A. Gershenfeld, Time Series Prediction: Forecasting the Future and Understanding the Past. Reading, MA: Addison-Wesley, 1994.
27. J. M. Corchado and J. Aiken, "Hybrid artificial intelligence methods in oceanographic forecast models," IEEE Trans. Syst., Man, Cybern., C, Appl. Rev., vol. 32, no. 4, pp. 307-313, Nov. 2002.
28. B. L. Zhan, R. Coggins, M. A. Jabri, D. Dersh, and B. Flower, "Multiresolution forecasting for futures trading using wavelet decompositions," IEEE Trans. Neural Netw., vol. 12, no. 4, pp. 765-775, Jul. 2001.
29. F. J. Chang, J. M. Liang, and Y. C. Chen, "Flood forecasting using radial basis function neural networks," IEEE Trans. Syst., Man, Cybern. C, Appl. Rev., vol. 31, no. 4, pp. 530-535, Nov. 2001.
30. S. J. Lee and C. L. Hou, "A neural-fuzzy system for congestion control in ATM networks," IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. 30, no. 1, pp. 2-9, Feb. 2000.
31. J. Dorronsoro, F. Ginel, C. Sanchez, and C. S. Cruz, "Neural fraud detection in credit card operations," IEEE Trans. Neural Netw., vol. 8, no. 4, pp. 827-834, Jul. 1997.
32. R. Logeswaran, "A prediction-based neural network scheme for lossless data compression," IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 32, no. 4, pp. 358-365, Nov. 2002.
33. V. Alarcon-Aquino and J. A. Barria, "Anomaly detection in communication networks using wavelets," Proc. Inst. Elect. Eng., Commun., vol. 148, no. 6, pp. 355-362, 2001.
34. V. Alarcon-Aquino, "Anomaly detection and prediction in communication networks using wavelet transforms," Ph.D. dissertation, Dept. Elect. Electron. Eng., Imperial College London, Univ. London, U.K., 2003.
35. R. Sitte and J. Sitte, "Analysis of the predictive ability of time delay neural networks applied to the S&P 500 time series," IEEE Trans. Syst., Man Cybern. C, Appl. Rev., vol. 30, no. 4, pp. 568-572, Aug. 2000.
36. L. J. Cao and F. E. H. Tay, "Support vector machine with adaptive parameters in financial time series forecasting," IEEE Trans. Neural Netw., vol. 14, no. 6, pp. 1506-1518, Nov. 2003.
37. J. L. Yuan and T. Fine, "Neural network design for small training sets of high dimensions," IEEE Trans. Neural Netw., vol. 9, no. 2, pp. 266-280, Mar. 1998.
38. C. Bishop, Neural Networks for Pattern Recognition, Oxford, U.K.: Oxford Univ. Press, 1995.