Tolerance of Radial Basis Functions Against Stuck-At-Faults

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.

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