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.
This work was supported by the Graduate College 776 – Automatic Configuration in Open Systems- funded by the German Research Foundation (DFG).
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Eickhoff, R., Rückert, U. (2006). Pareto-optimal Noise and Approximation Properties of RBF Networks. In: Kollias, S.D., Stafylopatis, A., Duch, W., Oja, E. (eds) Artificial Neural Networks – ICANN 2006. ICANN 2006. Lecture Notes in Computer Science, vol 4131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840817_103
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DOI: https://doi.org/10.1007/11840817_103
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