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