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Applied Computational Intelligence and Soft Computing
Volume 2012 (2012), Article ID 793176, 7 pages
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.

Citations to this Article [4 citations]

The following is the list of published articles that have cited the current article.

  • Sheila Esmeralda Gonzalez-Reyna, Juan Gabriel Avina-Cervantes, Sergio Eduardo Ledesma-Orozco, and Ivan Cruz-Aceves, “Eigen-Gradients for Traffic Sign Recognition,” Mathematical Problems in Engineering, vol. 2013, pp. 1–6, 2013. View at Publisher · View at Google Scholar
  • Engin Cemal MengÜÇ, and Nurettin Acir, “Real-Time Implementation of Lyapunov Stability Theory-Based Adaptive Filter on FPGA,” IEICE Transactions on Electronics, vol. E99.C, no. 1, pp. 129–137, 2016. View at Publisher · View at Google Scholar
  • Kh Islam, and Ram Raj, “Real-Time (Vision-Based) Road Sign Recognition Using an Artificial Neural Network,” Sensors, vol. 17, no. 4, pp. 853, 2017. View at Publisher · View at Google Scholar
  • Ahmed Madani, and Rubiyah Yusof, “Traffic sign recognition based on color, shape, and pictogram classification using support vector machines,” Neural Computing and Applications, 2017. View at Publisher · View at Google Scholar