Regularization theory and neural networks architectures
Neural Computation
Neural network fundamentals with graphs, algorithms, and applications
Neural network fundamentals with graphs, algorithms, and applications
Handbook of mathematics (3rd ed.)
Handbook of mathematics (3rd ed.)
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Design of Analog CMOS Integrated Circuits
Design of Analog CMOS Integrated Circuits
Feedforward sigmoidal networks - equicontinuity and fault-tolerance properties
IEEE Transactions on Neural Networks
Complete and partial fault tolerance of feedforward neural nets
IEEE Transactions on Neural Networks
Robustness of radial basis functions
Neurocomputing
A global model for fault tolerance of feedforward neural networks
ICAI'08 Proceedings of the 9th WSEAS International Conference on International Conference on Automation and Information
Fault Tolerance Improvement through Architecture Change in Artificial Neural Networks
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
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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.