Robustness of radial basis functions

Eickhoff R, Rückert U (2007)
Neurocomputing 70(16-18): 2758-2767.

Journal Article | Published | English

No fulltext has been uploaded

Author
;
Abstract
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.
Keywords
Publishing Year
ISSN
PUB-ID

Cite this

Eickhoff R, Rückert U. Robustness of radial basis functions. Neurocomputing. 2007;70(16-18):2758-2767.
Eickhoff, R., & Rückert, U. (2007). Robustness of radial basis functions. Neurocomputing, 70(16-18), 2758-2767.
Eickhoff, R., and Rückert, U. (2007). Robustness of radial basis functions. Neurocomputing 70, 2758-2767.
Eickhoff, R., & Rückert, U., 2007. Robustness of radial basis functions. Neurocomputing, 70(16-18), p 2758-2767.
R. Eickhoff and U. Rückert, “Robustness of radial basis functions”, Neurocomputing, vol. 70, 2007, pp. 2758-2767.
Eickhoff, R., Rückert, U.: Robustness of radial basis functions. Neurocomputing. 70, 2758-2767 (2007).
Eickhoff, Ralf, and Rückert, Ulrich. “Robustness of radial basis functions”. Neurocomputing 70.16-18 (2007): 2758-2767.
This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Search this title in

Google Scholar