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• W2339849993 abstract "The hierarchial network proposed (Multi-RBFNN), is composed of complete Radial Basis Function Neural Networks (RBFNNs) that are in charge of a reduced set of input variables with the property of which every Sub-RBFNN can take charge of a set of input variables and not of all. For the optimization of the whole net, we propose a new method to select the more important input variables, which is capable of deciding which of the chosen variables go alone or together to a Sub-RBFNN to build the hierarchic structure Multi-RBFNN, thus reducing the dimension of the input variable space for each RBFNN. We also provide an algorithm which automatically finds the most suitable topology of the proposed hierarchical structure and selects the more important input variables for it." @default.
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• W2339849993 date "2005-12-16" @default.
• W2339849993 modified "2023-09-26" @default.
• W2339849993 title "Hierarchical Structure for function approximation using radial basis function" @default.
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