Dilution in small Hopfield neural networks: computer models

Victor M. Castillo; Roger Dodd

doi:10.1117/12.140157

1 July 1992 Dilution in small Hopfield neural networks: computer models

Victor M. Castillo, Roger Dodd

Proceedings Volume 1710, Science of Artificial Neural Networks; (1992) https://doi.org/10.1117/12.140157
Event: Aerospace Sensing, 1992, Orlando, FL, United States

Abstract

The capacity of the Hopfield content-addressable neural network subject to a random dilution is investigated by numerical simulations. The sum-of-outer product learning rule is used to generate the synaptic weight matrix for the storage of M random, binary patterns. Randomly selected synaptic connection are then severed while the memory is probed to determine if the original patterns are still fixed. Other dilution methods are investigated such as one that leaves a Hamiltonian cycle, and one that does not allow isolation of nodes. In general, the critical dilution as a function of the loading ratio, (alpha) equals M/N, takes a sigmoid shape. The critical dilution is also a function of the network size and the sum of the effective Hamming distances between all of the fixed patterns.

Citation Download Citation

Victor M. Castillo and Roger Dodd "Dilution in small Hopfield neural networks: computer models", Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); https://doi.org/10.1117/12.140157

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