Approximating I/O data using radial basis functions: A new clustering-based approach

Abstract

In this paper, we deal with the problem of function approximation from a given set of input/output data. This problem consists of analyzing these training examples so that we can predict the output of the model given new inputs. We present a new method for function approximation of the I/O data using radial basis functions (RBFs). This approach is based on a new efficient method of clustering of the centres of the RBF Network (RBFN); it uses the objective output of the RBFN to move the clusters instead of just the input values of the I/O data. This method of clustering, especially designed for function approximation problems, improves the performance of the approximator system obtained, compared with other models derived from traditional algorithms. © Springer-Verlag Berlin Heidelberg 2005.