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Scientific Programming
Volume 2018, Article ID 3732120, 7 pages
https://doi.org/10.1155/2018/3732120
Research Article

An Incremental Optimal Weight Learning Machine of Single-Layer Neural Networks

1School of Computer & Computing Science, Zhejiang University City College, Hangzhou 310015, China
2College of Engineering, Lishui University, Lishui 323000, China
3School of Electronics and Information, Zhejiang University of Media and Communications, Hangzhou 310015, China

Correspondence should be addressed to Cheng-Bo Lu; moc.nuyila@obgnehc.ul

Received 12 October 2017; Revised 1 January 2018; Accepted 11 January 2018; Published 1 March 2018

Academic Editor: Wenbing Zhao

Copyright © 2018 Hai-Feng Ke 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. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, New York, NY, USA, 1995. View at MathSciNet
  2. G.-B. Huang and H. A. Babri, “Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions,” IEEE Transactions on Neural Networks and Learning Systems, vol. 9, no. 1, pp. 224–229, 1998. View at Publisher · View at Google Scholar · View at Scopus
  3. X.-F. Hu, Z. Zhao, S. Wang, F.-L. Wang, D.-K. He, and S.-K. Wu, “Multi-stage extreme learning machine for fault diagnosis on hydraulic tube tester,” Neural Computing and Applications, vol. 17, no. 4, pp. 399–403, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. T.-Y. Kwok and D.-Y. Yeung, “Objective functions for training new hidden units in constructive neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 8, no. 5, pp. 1131–1148, 1997. View at Publisher · View at Google Scholar · View at Scopus
  5. E. J. Teoh, K. C. Tan, and C. Xiang, “Estimating the number of hidden neurons in a feedforward network using the singular value decomposition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 17, no. 6, pp. 1623–1629, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Luo, J. Deng, W. Wang, J.-H. Wang, and W. Zhao, “A quantized kernel learning algorithm using a minimum kernel risk-sensitive loss criterion and bilateral gradient technique,” Entropy, vol. 19, no. 7, article no. 365, 2017. View at Publisher · View at Google Scholar · View at Scopus
  7. Y. Xu, X. Luo, W. Wang, and W. Zhao, “Efficient DV-HOP localization forwireless cyber-physical social sensing system: A correntropy-based neural network learning scheme,” Sensors, vol. 17, no. 1, article no. 135, 2017. View at Publisher · View at Google Scholar · View at Scopus
  8. S. Haykin, Neural networks and learning machines, Pearson, Prentice-Hall, New Jersey, USA, 3rd edition, 2009.
  9. S. Kumar, Neural Networks, McGraw-Hill Companies Inc., Columbus, OH, USA, 2006.
  10. X. Yao, “Evolving artificial neural networks,” Proceedings of the IEEE, vol. 87, no. 9, pp. 1423–1447, 1999. View at Publisher · View at Google Scholar · View at Scopus
  11. G. P. Zhang, “Neural networks for classification: a survey,” IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, vol. 30, no. 4, pp. 451–462, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. J. Hertz, A. Krogh, and R. G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Company, Boston, Mass, USA, 1991. View at MathSciNet
  13. 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
  14. G. Huang, L. Chen, and C. Siew, “Universal approximation using incremental constructive feedforward networks with random hidden nodes,” IEEE Transactions on Neural Networks and Learning Systems, vol. 17, no. 4, pp. 879–892, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. S.-F. Ding, X.-Z. Xu, and R. Nie, “Extreme learning machine and its applications,” Neural Computing and Applications, vol. 25, no. 3, pp. 549–556, 2014. View at Publisher · View at Google Scholar
  16. X. Luo, Y. Xu, W. Wang et al., “Towards enhancing stacked extreme learning machine with sparse autoencoder by correntropy,” Journal of The Franklin Institute, 2017. View at Publisher · View at Google Scholar
  17. M. Anthony and P. L. Bartlett, Neural Network Learning: Theoretical Foundations, Cambridge University Press, Cambridge, UK, 1999. View at MathSciNet
  18. V. N. Vapnik, Statistical Learning Theory, Adaptive and Learning Systems for Signal Processing, Communications, and Control, Wiley- Interscience, New York, NY, USA, 1998. View at MathSciNet
  19. L. Devroye, L. Györfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, vol. 31 of Stochastic Modelling and Applied Probability, Springer-Verlag New York, Berlin, Germany, 1996. View at Publisher · View at Google Scholar · View at MathSciNet
  20. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, Wiley-Interscience, New York, NY, USA, 2nd edition, 2001. View at MathSciNet
  21. L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone, Classification and Regression Trees, Wadsworth, Belmont, Mass, USA, 1984. View at MathSciNet
  22. Z. Man, K. Lee, D. Wang, Z. Cao, and S. Khoo, “An optimal weight learning machine for handwritten digit image recognition,” Signal Processing, vol. 93, no. 6, pp. 1624–1638, 2013. View at Publisher · View at Google Scholar · View at Scopus
  23. X. Luo, J. Deng, J. Liu, W. Wang, X. Ban, and J. Wang, “A quantized kernel least mean square scheme with entropy-guided learning for intelligent data analysis,” China Communications, vol. 14, no. 7, pp. 127–136, 2017. View at Publisher · View at Google Scholar
  24. W. Zhao, R. Lun, C. Gordon et al., “A human-centered activity tracking system: toward a healthier workplace,” IEEE Transactions on Human-Machine Systems, vol. 47, no. 3, pp. 343–355, 2017. View at Publisher · View at Google Scholar · View at Scopus
  25. G. Feng, G.-B. Huang, Q. Lin, and R. Gay, “Error minimized extreme learning machine with growth of hidden nodes and incremental learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 20, no. 8, pp. 1352–1357, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. G. H. Golub and C. F. van Loan, Matrix Computations, The Johns Hopkins University Press, Baltimore, Md, USA, 3rd edition, 1996. View at MathSciNet