About this Journal Submit a Manuscript Table of Contents
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 346949, 9 pages
http://dx.doi.org/10.1155/2013/346949
Research Article

Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network

Department of Computer Science and Engineering, Kongu Engineering College, Erode 638 052, India

Received 12 April 2013; Accepted 27 May 2013

Academic Editor: Ker-Wei Yu

Copyright © 2013 R. Manjula Devi 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. P. Mehra and B. W. Wah, Artificial Neural Networks: Concepts and Theory, IEEE Computer Society Press, 1992.
  2. R. P. Lippmann, “An introduction to computing with neural nets,” IEEE ASSP Magazine, vol. 4, no. 2, pp. 4–22, 1987. View at Publisher · View at Google Scholar · View at Scopus
  3. A. J. Owens, “Empirical modeling of very large data sets using neural networks,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '2000), vol. 6, pp. 302–307, July 2000. View at Scopus
  4. D. Nguyen and B. Widrow, “Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights,” in Proceedings of the International Joint Conference on Neural Networks (IJCNN '90), vol. 3, pp. 21–26, San Diego, Calif, USA, June 1990. View at Scopus
  5. T. M. Varnava and A. J. Meade Jr., “An initialization method for feedforward artificial neural networks using polynomial bases,” Advances in Adaptive Data Analysis, vol. 3, no. 3, pp. 385–400, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  6. L. Behera, S. Kumar, and A. Patnaik, “On adaptive learning rate that guarantees convergence in feedforward networks,” IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1116–1125, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. N. Ampazis and S. J. Perantonis, “Two highly efficient second-order algorithms for training feedforward networks,” IEEE Transactions on Neural Networks, vol. 13, no. 5, pp. 1064–1074, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. B. M. Wilamowski and H. Yu, “Improved computation for levenbergmarquardt training,” IEEE Transactions on Neural Networks, vol. 21, no. 6, pp. 930–937, 2010. View at Publisher · View at Google Scholar · View at Scopus
  9. H. Yu and B. M. Wilamowski, “Neural network training with second order algorithms,” Advances in Intelligent and Soft Computing, vol. 99, pp. 463–476, 2012. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Asuncion and D. J. Newman, “UCI Machine Learning Repository,” School of Information and Computer Science, University of California, Irvine, Calif, USA ,2007, http://www.ics.uci.edu/~mlearn/MLRepository.html.