Document Type

Journal Article


Hindawi Publishing Corporation


Faculty of Computing, Health and Science


School of Engineering / Centre for Communications Engineering Research




This article was originally published as: Lim, K., Seng, K., & Ang, L. K. (2012). MIMO Lyapunov Theory-Based RBF Neural Classifier for Traffic Sign Recognition. Applied Computational Intelligence and Soft Computing, 2012(1), Article ID 793176. Original article available here


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.

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