138 research outputs found
Robust Local Cluster Neural Networks (ESANN)
No full textEickhoff R, Sitte J, Rückert U. Robust Local Cluster Neural Networks (ESANN). In: Proceedings of the 14th European Symposium on Artificial Neural Networks (ESANN). Bruges, Belgium; 2006: 119-124.Artificial neural networks are intended to be used in future
nanoelectronics since their biological examples seem to be robust to
noise. In this paper, we analyze the robustness of Local Cluster Neural
Networks and determine upper bounds on the mean square error for noise
contaminated weights and inputs
Controlling complexity of RBF networks by similarity
No full textRückert U, Eickhoff R. Controlling complexity of RBF networks by similarity. In: ESANN. 2007: 181-186.Using radial basis function networks for function approximation
tasks suffers from unavailable knowledge about an adequate network
size. In this work, a measuring technique is proposed which can control the
model complexity and is based on the correlation coefficient between two
basis functions. Simulation results show good performance and, therefore,
this technique can be integrated in the RBF training procedure
Robustness of radial basis functions
No full textEickhoff R, Rückert U. Robustness of radial basis functions. Neurocomputing. 2007;70(16-18):2758-2767.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
Tolerance of Radial-Basis Functions Against Stuck-At-Faults
No full textEickhoff R, Rückert U. Tolerance of Radial-Basis Functions Against Stuck-At-Faults. In: Proceedings of the International Conference on Artificial Neural Networks (ICANN). Warsaw, Poland; 2005: 1003-1008.Neural networks are intended to be used in future nanoelectronic
systems since neural architectures seem to be robust against
malfunctioning elements and noise in their weights. In this paper we
analyze the fault-tolerance of Radial Basis Function networks to Stuck-
At-Faults at the trained weights and at the output of neurons. Moreover,
we determine upper bounds on the mean square error arising from these
faults
Pareto-optimal noise and approximation properties of RBFnetworks
No full textEickhoff R, Rückert U. Pareto-optimal noise and approximation properties of RBFnetworks. In: Kollias S, ed. Proceedings of the 16th International Conference on Artificial Neural Networks (ICANN). Athens, Greece: Springer Berlin Heidelberg; 2006: pp.:993-1002.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
Robustness of Radial Basis Functions
No full textEickhoff R, Rückert U. Robustness of Radial Basis Functions. In: Cabestany J, Prieto A, Sandoval DF, eds. Proceedings of the 8th International Work-Conference on Artificial Neural Networks (IWANN). Barcelona, Spain; 2005: 264-271.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
Schweden – auf dem weg zur großmacht
No full textAuthor for a specialist publication article to accompany the travelling museum exhibition, 1636- Ihre letzte schlacht. NB Sabine Eickhoff is erroneously accredited as co-author for undertaking the translation into German
Sonderfall Schottland – „ein starkes, dauerhafftigs volk“'
No full textAuthor for a specialist publication article to accompany the travelling museum exhibition, 1636- Ihre letzte schlacht. NB Sabine Eickhoff is erroneously accredited as co-author for undertaking the translation into German
Schottland – glaube und identität
No full textAuthor for a specialist publication article to accompany the travelling museum exhibition, 1636- Ihre letzte schlacht. NB Sabine Eickhoff is erroneously accredited as co-author for undertaking the translation into German
Historische Forschungen zu Schottischen Söldnern
No full textAuthor for a specialist publication article to accompany the travelling museum exhibition, 1636- Ihre letzte schlacht. NB Sabine Eickhoff is erroneously accredited as co-author for undertaking the translation into German
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