Pareto-optimal noise and approximation properties of RBFnetworks Eickhoff, Ralf Eickhoff Ralf Rückert, Ulrich Rückert Ulrich Neural networks are intended to be robust to noise and tolerant to failures in their architecture. Therefore, these systems are particularly interesting to be 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. pp.:993-1002 pp.:993-1002 Springer Berlin Heidelberg 2006