Pareto-optimal noise and approximation properties of RBF networks

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:
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Year:
2006

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Abstract

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.