The asymptotic memory capacity of the generalized Hopfield network.
basic_science · Level V
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Abstract
This paper presents a theoretical analysis on the asymptotic memory capacity of the generalized Hopfield network. The perceptron learning scheme is proposed to store sample patterns as the stable states in a generalized Hopfield network. We have obtained that (n-1) and 2n are a lower and an upper bound of the asymptotic memory capacity of the network of n neurons, respectively, which shows that the generalized Hopfield network can store the larger number of sample patterns than Hopfield network.