Associative Memory: An Optimum Binary Neuron Representation

A. A. S. Awwal; M. A. Karim; H. K. Liu

doi:10.1117/12.951512

29 June 1989 Associative Memory: An Optimum Binary Neuron Representation

A. A. S. Awwal, M. A. Karim, H. K. Liu

Proceedings Volume 1053, Optical Pattern Recognition; (1989) https://doi.org/10.1117/12.951512
Event: OE/LASE '89, 1989, Los Angeles, CA, United States

Abstract

Convergence mechanism of vectors in the Hopfield's neural network is studied in terms of both weights (i.e., inner products) and Hamming distance. It is shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, weights (which in turn depend on the neuron representation) are found to play a more dominant role in the convergence mechanism. Consequently, a new binary neuron representation for associative memory is proposed. With the new neuron representation, the associative memory responds unambiguously to the partial input in retrieving the stored information.

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A. A. S. Awwal, M. A. Karim, and H. K. Liu "Associative Memory: An Optimum Binary Neuron Representation", Proc. SPIE 1053, Optical Pattern Recognition, (29 June 1989); https://doi.org/10.1117/12.951512

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