About this Journal Submit a Manuscript Table of Contents
Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 793176, 7 pages
http://dx.doi.org/10.1155/2012/793176
Research Article

MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition

1Electrical and Computer Department, School of Engineering and Science, Curtin University, Sarawak Malaysia, CDT 250, 98009 Miri Sarawak, Malaysia
2School of Computer Technology, Sunway University, No. 5, Jalan Universiti, Bandar Sunway, Selangor Darul Ehsan, 46150 Petaling, Malaysia
3Centre for Communications Engineering Research, Edith Cowan University, Joondalup, WA 6027, Australia

Received 27 October 2011; Revised 21 February 2012; Accepted 22 February 2012

Academic Editor: Toly Chen

Copyright © 2012 King Hann Lim 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. Y. Y. Nguwi and A. Z. Kouzani, “Detection and classification of road signs in natural environments,” Neural Computing and Applications, vol. 17, no. 3, pp. 265–289, 2008. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Shao, Q. Chen, and H. Jiang, “RBF neural network based on particle swarm optimization,” in Proceedings of the 7th International Conference on Advances in Neural Networks (ISNN '10), L. Zhang, et al., Ed., vol. 6063, pp. 169–176, Springer, Shanghai, China, 2010. View at Publisher · View at Google Scholar
  3. G. A. P. Coronado, M. R. Muñoz, J. M. Armingol et al., “Road sign recognition for automatic inventory systems,” in Proceedings of the 18th International Conference on Systems, Signals, and Image Processing (IWSSIP '11), pp. 63–66, 2011.
  4. K. H. Lim, K. P. Seng, and L.-M. Ang, “Improved traffic sign recognition,” in Proceedings of the International Conference on Embedded Systems and Intelligent Technology (ICESIT '10), Chiang Mai, Thailand, 2010.
  5. J. Park and I. W. Sandberg, “Universal approximation using radial-basis-function networks,” Neural Computation, vol. 3, no. 2, pp. 246–257, 1991.
  6. S. Lee and R. M. Kil, “A gaussian potential function network with hierarchically self-organizing learning,” Neural Networks, vol. 4, no. 2, pp. 207–224, 1991. View at Scopus
  7. M. S. Mueller, “Least-squares algorithms for adaptive equalizers,” The Bell System Technical Journal, vol. 60, no. 8, pp. 1905–1925, 1981. View at Scopus
  8. Z. H. Man, et al., “Design of robust adaptive filters using Lyapunov stability theory,” in Proceedings of the IEEE Transactions on Circuits & Systems II: Express Briefs, 2004.
  9. K. P. Seng, Z. Man, and H. R. Wu, “Lyapunov-theory-based radial basis function networks for adaptive filtering,” IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 49, no. 8, pp. 1215–1220, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. K. H. Lim, K. P. Seng, L. M. Ang, and S. W. Chin, “Lyapunov theory-based multilayered neural network,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 56, no. 4, pp. 305–309, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice Hall, 1994.
  12. M. J. Er, S. Wu, J. Lu, and H. L. Toh, “Face recognition with radial basis function (RBF) neural networks,” IEEE Transactions on Neural Networks, vol. 13, no. 3, pp. 697–710, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. M. J. Er, W. Chen, and S. Wu, “High-speed face recognition based on discrete cosine transform and rbf neural networks,” IEEE Transactions on Neural Networks, vol. 16, no. 3, pp. 679–691, 2005. View at Publisher · View at Google Scholar · View at Scopus
  14. J. A. Leonard and M. A. Kramer, “Radial basis function networks for classifying process faults,” IEEE Control Systems Magazine, vol. 11, no. 3, pp. 31–38, 1991. View at Scopus
  15. M. John and J. D. Christian, “Fast learning in networks of locally-tuned processing units,” Neural Computation, vol. 1, no. 2, pp. 281–294, 1989.
  16. F. Yang and M. Paindavoine, “Implementation of an rbf neural network on embedded systems: real-time face tracking and identity verification,” IEEE Transactions on Neural Networks, vol. 14, no. 5, pp. 1162–1175, 2003. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Kishan, Elements of Artificial Neural Networks, MIT Press, 1997.
  18. V. Espinosa-Duro, “Biometric identification system using a radial basis network,” in Proceedings of the 34th IEEE Annual International Carnahan Conference on Security Technology, pp. 47–51, 2000.
  19. M. Birgmeier, “Fully kalman-trained radial basis function network for nonlinear speech modeling,” in Proceedings of the IEEE International Conference on Neural Networks, pp. 259–264, December 1995. View at Scopus