Robustness of radial basis functions

Authors:
Ralf Eickhoff;Ulrich Rückert
Affiliations:
Heinz Nixdorf Institute, System and Circuit Technology, University of Paderborn, Germany;Heinz Nixdorf Institute, System and Circuit Technology, University of Paderborn, Germany
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

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Abstract

Neural networks are intended to be used in future nanoelectronics 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.