Table of Contents Author Guidelines Submit a Manuscript
Discrete Dynamics in Nature and Society
Volume 2016 (2016), Article ID 4135056, 11 pages
http://dx.doi.org/10.1155/2016/4135056
Research Article

A Network Traffic Prediction Model Based on Quantum-Behaved Particle Swarm Optimization Algorithm and Fuzzy Wavelet Neural Network

School of Mathematics and Statistics, Chuxiong Normal University, Chuxiong, Yunnan 675000, China

Received 2 November 2015; Revised 14 January 2016; Accepted 3 February 2016

Academic Editor: Ahmed Kattan

Copyright © 2016 Kun Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. H. Mahmassani, “Dynamic network traffic assignment and simulation methodology for advanced system management applications,” Networks and Spatial Economics, vol. 1, no. 3, pp. 267–292, 2001. View at Publisher · View at Google Scholar
  2. A. Nogueira, P. Salvador, R. Valadas et al., “Markovian modelling of internet traffic,” in Network Performance Engineering, vol. 5233 of Lecture Notes in Computer Science, pp. 98–124, Springer, Berlin, Germany, 2011. View at Publisher · View at Google Scholar
  3. S. Y. Chang and H.-C. Wu, “Novel fast computation algorithm of the second-order statistics for autoregressive moving-average processes,” IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 526–535, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. M.-C. Tan, S. C. Wong, J.-M. Xu, Z.-R. Guan, and P. Zhang, “An aggregation approach to short-term traffic flow prediction,” IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, pp. 60–69, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. Y. Shu, Z. Jin, L. Zhang, L. Wang, and O. W. W. Yang, “Traffic prediction using FARIMA models,” in Proceedings of the IEEE International Conference on Communications (ICC '99), vol. 2, pp. 891–895, IEEE, Vancouver, Canada, June 1999. View at Publisher · View at Google Scholar
  6. R. Li, J.-Y. Chen, Y.-J. Liu, and Z.-K. Wang, “WPANFIS: combine fuzzy neural network with multiresolution for network traffic prediction,” Journal of China Universities of Posts and Telecommunications, vol. 17, no. 4, pp. 88–93, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. K. Zhao, “Global robust exponential synchronization of BAM recurrent FNNs with infinite distributed delays and diffusion terms on time scales,” Advances in Difference Equations, vol. 2014, article 317, 2014. View at Publisher · View at Google Scholar · View at MathSciNet
  8. E. A. Rying, G. L. Bilbro, and J.-C. Lu, “Focused local learning with wavelet neural networks,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 304–319, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Lin, X. Wang, F. Nian, and Y. Zhang, “Dynamic fuzzy neural networks modeling and adaptive backstepping tracking control of uncertain chaotic systems,” Neurocomputing, vol. 73, no. 16–18, pp. 2873–2881, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. R. H. Abiyev and O. Kaynak, “Fuzzy wavelet neural networks for identification and control of dynamic plants—a novel structure and a comparative study,” IEEE Transactions on Industrial Electronics, vol. 55, no. 8, pp. 3133–3140, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. H. Iyatomi and M. Hagiwara, “Adaptive fuzzy inference neural network,” Pattern Recognition, vol. 37, no. 10, pp. 2049–2057, 2004. View at Publisher · View at Google Scholar · View at Scopus
  12. C.-H. Lee and C.-C. Teng, “Identification and control of dynamic systems using recurrent fuzzy neural networks,” IEEE Transactions on Fuzzy Systems, vol. 8, no. 4, pp. 349–366, 2000. View at Publisher · View at Google Scholar · View at Scopus
  13. W. Li and Y. Hori, “An algorithm for extracting fuzzy rules based on RBF neural network,” IEEE Transactions on Industrial Electronics, vol. 53, no. 4, pp. 1269–1276, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. G. K. Venayagamoorthy, “Online design of an echo state network based wide area monitor for a multimachine power system,” Neural Networks, vol. 20, no. 3, pp. 404–413, 2007. View at Publisher · View at Google Scholar · View at Scopus
  15. W. Fang, J. Sun, and W. Xu, “A new mutated quantum-behaved particle swarm optimizer for digital IIR filter design,” EURASIP Journal on Advances in Signal Processing, vol. 2009, Article ID 367465, 7 pages, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. L. dos Santos Coelho and V. C. Mariani, “Particle swarm approach based on quantum mechanics and harmonic oscillator potential well for economic load dispatch with valve-point effects,” Energy Conversion and Management, vol. 49, no. 11, pp. 3080–3085, 2008. View at Publisher · View at Google Scholar · View at Scopus
  17. L. D. S. Coelho, “Gaussian quantum-behaved particle swarm optimization approaches for constrained engineering design problems,” Expert Systems with Applications, vol. 37, no. 2, pp. 1676–1683, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. R. Poli, “Analysis of the publications on the applications of particle swarm optimisation,” Journal of Artificial Evolution and Applications, vol. 2008, Article ID 685175, 10 pages, 2008. View at Publisher · View at Google Scholar
  19. R. C. Eberhart and Y. Shi, Computational Intelligence: Concepts to Implementations, Elsevier, Philadelphia, Pa, USA, 2009.
  20. S. L. Sabat, L. dos Santos Coelho, and A. Abraham, “MESFET DC model parameter extraction using Quantum Particle Swarm Optimization,” Microelectronics Reliability, vol. 49, no. 6, pp. 660–666, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. J. Sun, B. Feng, and W. Xu, “Particle swarm optimization with particles having quantum behavior,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '04), pp. 325–331, Portland, Ore, USA, June 2004. View at Scopus
  22. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  23. J. Sun, W. Fang, V. Palade, X. Wu, and W. Xu, “Quantum-behaved particle swarm optimization with Gaussian distributed local attractor point,” Applied Mathematics and Computation, vol. 218, no. 7, pp. 3763–3775, 2011. View at Publisher · View at Google Scholar · View at Scopus
  24. S. Mallate, A Wavelet Tour of Signal Processing: The Sparse Way, Elsevier, Philadelphia, Pa, USA, 2009.
  25. F.-J. Lin, C.-H. Lin, and P.-H. Shen, “Self-constructing fuzzy neural network speed controller for permanent-magnet synchronous motor drive,” IEEE Transactions on Fuzzy Systems, vol. 9, no. 5, pp. 751–759, 2001. View at Publisher · View at Google Scholar · View at Scopus
  26. S. M. Mikki and A. A. Kishk, “Quantum particle swarm optimization for electromagnetics,” IEEE Transactions on Antennas and Propagation, vol. 54, no. 10, pp. 2764–2775, 2006. View at Publisher · View at Google Scholar · View at Scopus
  27. F. H. T. Vieira, G. R. Bianchi, and L. L. Lee, “A network traffic prediction approach based on multifractal modeling,” Journal of High Speed Networks, vol. 17, no. 2, pp. 83–96, 2010. View at Publisher · View at Google Scholar · View at Scopus
  28. M. Barabas, G. Boanea, A. B. Rus, V. Dobrota, and J. Domingo-Pascual, “Evaluation of network traffic prediction based on neural networks with multi-task learning and multiresolution decomposition,” in Proceedings of the IEEE 7th International Conference on Intelligent Computer Communication and Processing (ICCP '11), pp. 95–102, Cluj-Napoca, Romania, August 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. Y. Liang, “Real-time VBR video traffic prediction for dynamic bandwidth allocation,” IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, vol. 34, no. 1, pp. 32–47, 2004. View at Publisher · View at Google Scholar · View at Scopus
  30. J.-T. Tsai, T.-K. Liu, and J.-H. Chou, “Hybrid taguchi-genetic algorithm for global numerical optimization,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 4, pp. 365–377, 2004. View at Publisher · View at Google Scholar · View at Scopus
  31. K. Papagiannaki, N. Taft, Z.-L. Zhang, and C. Diot, “Long-term forecasting of Internet backbone traffic,” IEEE Transactions on Neural Networks, vol. 16, no. 5, pp. 1110–1124, 2005. View at Publisher · View at Google Scholar · View at Scopus