Universal approximation using radial-basis-function networks
Neural Computation
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Detection and classification of road signs in natural environments
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Lyapunov theory-based multilayered neural network
IEEE Transactions on Circuits and Systems II: Express Briefs
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ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Face recognition with radial basis function (RBF) neural networks
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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.