Robustness of Radial Basis Functions

Eickhoff R, Rückert U (2006)
In: Neurocomputing.

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Conference Paper | Published | English

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Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment. Furthermore, upper bounds on the mean square error under noise contaminated parameters and inputs are determined if the network parameters are constrained. To achieve robuster neural network architectures fundamental methods are introduced to identify sensitive parameters and neurons.
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Eickhoff R, Rückert U. Robustness of Radial Basis Functions. In: Neurocomputing. 2006.
Eickhoff, R., & Rückert, U. (2006). Robustness of Radial Basis Functions. Neurocomputing. doi:10.1016/j.neucom.2006.04.012
Eickhoff, R., and Rückert, U. (2006). “Robustness of Radial Basis Functions” in Neurocomputing.
Eickhoff, R., & Rückert, U., 2006. Robustness of Radial Basis Functions. In Neurocomputing.
R. Eickhoff and U. Rückert, “Robustness of Radial Basis Functions”, Neurocomputing, 2006.
Eickhoff, R., Rückert, U.: Robustness of Radial Basis Functions. Neurocomputing. (2006).
Eickhoff, Ralf, and Rückert, Ulrich. “Robustness of Radial Basis Functions”. Neurocomputing. 2006.
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