TY  - BOOK
AB  - 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.
DA  - 2005
LA  - eng
PY  - 2005
TI  - Tolerance of Radial-Basis Functions Against Stuck-At-Faults
UR  - https://nbn-resolving.org/urn:nbn:de:0070-pub-22888376
Y2  - 2024-11-22T03:47:10
ER  -