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A Radial Basis Function (RBF) neural network depends mainly upon an adequate choice of the number and positions of its basis function centers. In this paper, the authors have proposed an algorithm for RBF neural network and the results may be reduced for artificial neural networks as particular cases. The radial basis function neural networks are powerful function approximators for multivariate nonlinear continuous mappings. They have a simple architecture and the learning algorithm corresponds to the solution of a linear regression problem.
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