Input Variable Selection Using Parallel Processing of RBF Neural Networks

In this paper we propose a new technique focused on the selection of the important input variable for modellingcomplex systems of function approximation problems, in order to avoid the exponential increase in the complexity of thesystem that is usual when dealing with many input variables. The proposed parallel processing approach is composed ofcomplete radial basis function neural networks that are in charge of a reduced set of input variables depending in the generalbehaviour of the problem. For the optimization of the parameters of each radial basis function neural networks in the system, we propose a new method to select the more important input variables which is capable of deciding which of the chosenvariables go alone or together to each radial basis function neural networks to build the parallel structure, thus reducing thedimension of the input variable space for each radial basis function neural networks. We also provide an algorithm whichautomatically finds the most suitable topology of the proposed parallel processing structure and selects the more importantinput variables for it. Therefore, our goal is to find the most suitable of the proposed families of parallel processingarchitectures in order to approximate a system from which a set of input/output. So that the proposed parallel processingstructure outperforms other algorithms not only with respect to the final approximation error but also with respect to thenumber of computation parameters of the system