In this paper, a new approach is proposed to determine the structure of radial basis function (RBF) networks. This approach starts with an enough number of hidden nodes and reduces the number of nodes in the course of learning. The algorithm can be employed in the problems where only the performance index of the network output is given, as well as in the supervised training problems where the desired output values are available. Also, it is applicable to either of classification problems and function approximation problems.
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