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Computational Intelligence and Neuroscience
Volume 2014, Article ID 270658, 10 pages
http://dx.doi.org/10.1155/2014/270658
Research Article

Research on WNN Modeling for Gold Price Forecasting Based on Improved Artificial Bee Colony Algorithm

School of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China

Received 29 October 2013; Revised 19 November 2013; Accepted 11 December 2013; Published 13 February 2014

Academic Editor: Jianwei Shuai

Copyright © 2014 Bai Li. 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. Y. Long, “Visibility graph network analysis of gold price time series,” Physica A, vol. 392, no. 16, pp. 3374–3384, 2013. View at Publisher · View at Google Scholar
  2. A. Parisi, F. Parisi, and D. Díaz, “Forecasting gold price changes: rolling and recursive neural network models,” Journal of Multinational Financial Management, vol. 18, no. 5, pp. 477–487, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. S. Shafiee and E. Topal, “An overview of global gold market and gold price forecasting,” Resources Policy, vol. 35, no. 3, pp. 178–189, 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. X. Shao, K. Wu, and B. Liao, “p-norm multikernel learning approach for stock market price forecasting,” Computational Intelligence and Neuroscience, vol. 2012, Article ID 601296, 10 pages, 2012. View at Publisher · View at Google Scholar
  5. Q. H. Do and J.-F. Chen, “A neuro-fuzzy approach in the classification of students’ academic performance,” Computational Intelligence and Neuroscience, vol. 2013, Article ID 179097, 7 pages, 2013. View at Publisher · View at Google Scholar
  6. F. M. Dias, A. Antunes, and A. M. Mota, “Artificial neural networks: a review of commercial hardware,” Engineering Applications of Artificial Intelligence, vol. 17, no. 8, pp. 945–952, 2004. View at Publisher · View at Google Scholar · View at Scopus
  7. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115–133, 1943. View at Publisher · View at Google Scholar · View at Scopus
  8. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Google Scholar · View at Scopus
  9. D. Lowe and D. S. Broomhead, “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355, 1988. View at Google Scholar
  10. I. Daubechies, “Wavelet transform, time-frequency localization and signal analysis,” IEEE Transactions on Information Theory, vol. 36, no. 5, pp. 961–1005, 1990. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Kohonen, “The self-organizing map,” Proceedings of the IEEE, vol. 78, no. 9, pp. 1464–1480, 1990. View at Publisher · View at Google Scholar · View at Scopus
  12. J. J. Hopfield and D. W. Tank, “‘Neural’ computation of decisions in optimization problems,” Biological Cybernetics, vol. 52, no. 3, pp. 141–152, 1985. View at Google Scholar · View at Scopus
  13. V. Kreinovich, O. Sirisaengtaksin, and S. Cabrera, “Wavelet neural networks are asymptotically optimal approximators for functions of one variable,” in Proceedings of the IEEE World Congress on Computational Intelligence, vol. 1, pp. 299–304, Orlando, Fla, USA, June 1994. View at Publisher · View at Google Scholar · View at Scopus
  14. Y. Yan and B. Guo, “Application of wavelet neural network (WNN) and gradient descent method (GDM) in natural image denoising,” Journal of Computational Information Systems, vol. 2, no. 2, pp. 625–631, 2006. View at Google Scholar · View at Scopus
  15. M. Yue-bo, Z. Jian-hua, G. Xu-sheng, and Z. Liang, “Research on WNN aerodynamic modeling from flight data based on improved PSO algorithm,” Neurocomputing, vol. 83, pp. 212–221, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Karaboga, “An idea based on honey bee swarm for numerical optimization,” Tech. Rep. TR06, Erciyes University Press, Kayseri, Turkey, 2005. View at Google Scholar
  17. G. Zhu and S. Kwong, “Gbest-guided artificial bee colony algorithm for numerical function optimization,” Applied Mathematics and Computation, vol. 217, no. 7, pp. 3166–3173, 2010. View at Publisher · View at Google Scholar · View at Scopus
  18. F. Kang, J. Li, and Z. Ma, “Rosenbrock artificial bee colony algorithm for accurate global optimization of numerical functions,” Information Sciences, vol. 181, no. 16, pp. 3508–3531, 2011. View at Publisher · View at Google Scholar · View at Scopus
  19. W.-F. Gao, S.-Y. Liu, and L.-L. Huang, “A novel artificial bee colony algorithm with Powell's method,” Applied Soft Computing, vol. 13, no. 9, pp. 3763–3775, 2013. View at Publisher · View at Google Scholar
  20. B. Li and Y. Li, “BE-ABC: hybrid artificial bee colony algorithm with balancing evolution strategy,” in Proceedings of the 3rd International Conference on Intelligent Control and Information Processing (ICICIP '12), pp. 217–222, Dalian, China, July 2012. View at Publisher · View at Google Scholar
  21. A. Banharnsakun, B. Sirinaovakul, and T. Achalakul, “Job shop scheduling with the best-so-far ABC,” Engineering Applications of Artificial Intelligence, vol. 25, no. 3, pp. 583–593, 2012. View at Publisher · View at Google Scholar · View at Scopus
  22. M. Manuel and E. Elias, “Design of frequency response masking FIR filter in the canonic signed digit space using modified artificial bee colony algorithm,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 660–668, 2012. View at Publisher · View at Google Scholar · View at Scopus
  23. B. Li, L. G. Gong, and C. Zhao, “Unmanned combat aerial vehicles path planning using a novel probability density model based on artificial bee colony algorithm,” in Proceedings of the 4th International Conference on Intelligent Control and Information Processing (ICICIP '13), pp. 620–625, Beijing, China, June 2013. View at Publisher · View at Google Scholar
  24. B. Li, L. G. Gong, and Y. Yao, “On the performance of internal feedback artificial bee colony algorithm (IF-ABC) for protein secondary structure prediction,” in Proceedings of the 6th International Conference on Advanced Computational Intelligence, pp. 33–38, Hangzhou, China, October 2013.
  25. S. F. M. Hussein, M. B. N. Shah, M. R. A. Jalal, and S. S. Abdullah, “Gold price prediction using radial basis function neural network,” in Proceedings of the 4th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO '11), pp. 1–11, Kuala Lumpur, Malaysia, April 2011. View at Publisher · View at Google Scholar · View at Scopus
  26. C. Liu, “Price forecast for gold futures based on GA-BP neural network,” in Proceedings of the International Conference on Management and Service Science (MASS '09), pp. 1–4, Wuhan, China, September 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. Z. Jinliang, T. Mingming, and T. Mingxin, “Effects simulation of international gold prices on crude oil prices based on WBNNK model,” in Proceedings of the ISECS International Colloquium on Computing, Communication, Control, and Management (CCCM '09), vol. 4, pp. 459–463, Sanya, China, August 2009. View at Publisher · View at Google Scholar · View at Scopus
  28. Y. Tian, Hybrid Neural Network Technology, Sciences Press, Beijing, China, 2009.
  29. B. Li, Y. Li, and L. G. Gong, “Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model,” Engineering Applications of Artificial Intelligence, vol. 27, pp. 70–79, 2014. View at Publisher · View at Google Scholar
  30. E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, “GSA: a gravitational search algorithm,” Information Sciences, vol. 179, no. 13, pp. 2232–2248, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. Jian-Hui and D. Wei, “Prediction of gold price based on WT-SVR and EMD-SVR model,” in Proceedings of the 8th International Conference on Computational Intelligence and Security (CIS '12), pp. 415–419, Guangzhou, China, November 2012. View at Publisher · View at Google Scholar
  32. G. Grudnitski and L. Osburn, “Forecasting S&P and gold futures prices: an application of neural networks,” Journal of Futures Markets, vol. 13, no. 6, pp. 631–643, 1993. View at Publisher · View at Google Scholar
  33. L. Shuguang and H. Zaiyong, “An analysis: the stability of long-run determinants of the gold price,” World Economy Study, vol. 2, no. 9, 2008. View at Google Scholar
  34. F. Zhang and Z. Liao, “Gold price forecasting based on RBF neural network and hybrid fuzzy clustering algorithm,” in Proceedings of the 7th International Conference on Management Science and Engineering Management, vol. 241 of Lecture Notes in Electrical Engineering, pp. 73–84, Springer, Berlin, Germany, 2014. View at Publisher · View at Google Scholar