An enhanced clustering function approximation technique for a radial basis function neural network

The majority of clustering methods that have been proposed in the literature are focused on classification problems, and more specifically on pattern recognition problems. In this paper, we provide an enhanced clustering function approximation (ECFA) methodology that is especially suited for function approximation problems, which calculates the error committed in every cluster using the real output of the radial basis function neural network (RBFN), and not just an approximate value of that output, trying to concentrate more clusters in those input regions where the approximation error is bigger, thus attempting to homogenize the contribution to the error of every cluster. ECFA migrates clusters to the zones of the input space where the approximation error is bigger, thus trying to homogenously distribute the total distortion in every cluster, producing a better share-out of clusters for the data input space. We will see that ECFA outperforms other clustering techniques, such as the FCM algorithm or the CFA algorithm not only with respect to the final approximation error but also with respect to the execution time.