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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.

Abstract

Lyapunov theory-based radial basis function neural network (RBFNN) is developed for traffic sign recognition in this paper to perform multiple inputs multiple outputs (MIMO) classification. Multidimensional input is inserted into RBF nodes and these nodes are linked with multiple weights. An iterative weight adaptation scheme is hence designed with regards to the Lyapunov stability theory to obtain a set of optimum weights. In the design, the Lyapunov function has to be well selected to construct an energy space with a single global minimum. Weight gain is formed later to obey the Lyapunov stability theory. Detail analysis and discussion on the proposed classifier’s properties are included in the paper. The performance comparisons between the proposed classifier and some existing conventional techniques are evaluated using traffic sign patterns. Simulation results reveal that our proposed system achieved better performance with lower number of training iterations.