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. 1. Introduction Traffic sign recognition is important in autonomous vehicular technology for the sake of identifying a sign functionality through visual information capturing via sensors. The usage of neural networks has become increasingly popular in traffic sign recognition recently to classify various kinds of traffic signs into a specific category [1–3]. The reason of applying neural networks in traffic sign recognition is that, they can incorporate both statistical and structural information to achieve better performance than a simple minimum distance classifier [4]. The adaptive learning capability and processing parallelism for complex problems have led to the rapid advancement of neural networks. Among all neural networks, radial basis function neural network (RBFNN) has been applied in many engineering applications with the following significant properties: (i) universal approximators [5]; (ii) simple topological structure [6] which allows straightforward computation using a linearly weighted combination of single hidden-layer neurons. The learning characteristic of RBFNN is greatly related to the associative weights between hidden-output nodes. Therefore, an optimal algorithm is required to update the weights relative to an arbitrary training input. Conventionally, the training process for RBFNN is mainly dependent on the optimization theory. The cost function of this network, for instance, the sum of squared errors or mean squared error between network’s output and targeted input is firstly defined. It is followed by minimizing the cost function in weight parameter space to search
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.
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.
G. A. P. Coronado, M. R. Mu？oz, 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.