The capacity of the Hopfield model has been
considered as an imortant parameter in using this
model. In this paper, the Hopfield neural network is
modeled as a Shannon Channel and an upperbound
to its capacity is found.
For achieving maximum memory, we focus
on the training algorithm of the network, and prove
that the capacity of the network is bounded by the
maximum number of the orthogonal training
patterns. Then, the pratical memory of the network,
for noiseless and noisy inputs, by appropiate coding
of the training patterns is examined. Finally, the
theoretical results and the increase rate of the
memory is evaluated by simulation
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