Pareto-optimal noise and approximation properties of RBF networks

Abstract

Neural networks are intended to be robust to noise and tolerant to failures in their architecture. Therefore, these systems are particularly interesting to lie integrated in hardware and to be operating under noisy environment. In this work, measurements are introduced which can decrease the sensitivity of Radial Basis Function networks to noise without any degradation in their approximation capability. For this purpose, pareto-optimal solutions are determined for the parameters of the network. © Springer-Verlag Berlin Heidelberg 2006.