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In this paper the authors propose a new technique focused on the selection of the important input variable for modeling complex systems of function approximation problems, in order to avoid the exponential increase in the complexity of the system that is usual when dealing with many input variables. The proposed parallel processing approach is composed of complete radial basis function neural networks that are in charge of a reduced set of input variables depending in the general behaviour of the problem.
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