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Mathematical Problems in Engineering
Volume 2015, Article ID 946292, 9 pages
http://dx.doi.org/10.1155/2015/946292
Research Article

The Optimisation for Local Coupled Extreme Learning Machine Using Differential Evolution

Information Science and Technology College, Dalian Maritime University, Dalian 116026, China

Received 13 August 2014; Revised 12 November 2014; Accepted 24 November 2014

Academic Editor: Yi Jin

Copyright © 2015 Yanpeng Qu and Ansheng Deng. 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. G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: theory and applications,” Neurocomputing, vol. 70, no. 1–3, pp. 489–501, 2006. View at Publisher · View at Google Scholar · View at Scopus
  2. G.-B. Huang, D. H. Wang, and Y. Lan, “Extreme learning machines: a survey,” International Journal of Machine Learning and Cybernetics, vol. 2, no. 2, pp. 107–122, 2011. View at Publisher · View at Google Scholar · View at Scopus
  3. Y. Qu, C. Shang, W. Wu, and Q. Shen, “Evolutionary fuzzy extreme learning machine for mammographic risk analysis,” International Journal of Fuzzy Systems, vol. 13, no. 4, pp. 282–291, 2011. View at Google Scholar · View at MathSciNet · View at Scopus
  4. Y. Yang, Y. Wang, and X. Yuan, “Bidirectional extreme learning machine for regression problem and its learning effectiveness,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, pp. 1498–1505, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411–1423, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. Q.-Y. Zhu, A. K. Qin, P. N. Suganthan, and G.-B. Huang, “Evolutionary extreme learning machine,” Pattern Recognition, vol. 38, no. 10, pp. 1759–1763, 2005. View at Publisher · View at Google Scholar · View at Scopus
  7. G.-B. Huang and L. Chen, “Convex incremental extreme learning machine,” Neurocomputing, vol. 70, no. 16–18, pp. 3056–3062, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. H. Rong, Y.-S. Ong, A.-H. Tan, and Z. Zhu, “A fast pruned-extreme learning machine for classification problem,” Neurocomputing, vol. 72, no. 1–3, pp. 359–366, 2008. View at Google Scholar
  9. Y. Lan, Y. C. Soh, and G.-B. Huang, “Two-stage extreme learning machine for regression,” Neurocomputing, vol. 73, no. 16–18, pp. 3028–3038, 2010. View at Publisher · View at Google Scholar · View at Scopus
  10. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  11. H.-J. Rong, G.-B. Huang, N. Sundararajan, and P. Saratchandran, “On-line sequential fuzzy extreme learning machine for function approximation and classification problems,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 39, no. 4, pp. 1067–1072, 2009. View at Publisher · View at Google Scholar · View at Scopus
  12. G.-B. Huang, L. Chen, and C.-K. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. T. Back, Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, Oxford, UK, 1996. View at MathSciNet
  14. S. Das and P. N. Suganthan, “Differential evolution: a survey of the state-of-the-art,” IEEE Transactions on Evolutionary Computation, vol. 15, no. 1, pp. 4–31, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. B. Subudhi and D. Jena, “A differential evolution based neural network approach to nonlinear system identification,” Applied Soft Computing, vol. 11, no. 1, pp. 861–871, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. M. G. Genton, “Classes of kernels for machine learning: a statistics perspective,” The Journal of Machine Learning Research, vol. 2, pp. 299–312, 2001. View at Google Scholar · View at MathSciNet
  17. R. Jensen and Q. Shen, “New approaches to fuzzy-rough feature selection,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 4, pp. 824–838, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. K. Bache and M. Lichman, “UCI machine learning repository,” 2013.
  19. M. Mike, Statistical Datasets, Department of Statistics, Carnegie Mellon University, 1989.
  20. Y. Qu, C. Shang, Q. Shen, N. M. Parthaláin, and W. Wu, “Kernel-based fuzzy-rough nearest neighbour classification,” in Proceedings of the IEEE International Conference on Fuzzy Systems (FUZZ '11), pp. 1523–1529, June 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Shang, D. Barnes, and Q. Shen, “Facilitating efficient mars terrain image classification with fuzzy-rough feature selection,” International Journal of Hybrid Intelligent Systems, vol. 8, no. 1, pp. 3–13, 2011. View at Google Scholar