Robustness of Radial Basis Functions

摘要

Neural networks are intended to be used in future nanoelec-tronics since these architectures seem to be robust against malfunctioning elements and noise. In this paper we analyze the robustness of radial basis function networks and determine upper bounds on the mean square error under noise contaminated weights and inputs.