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Associative memory - An optimum binary neuron representationConvergence 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.
Document ID
19900045048
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Awwal, A. A.
(Dayton Univ. OH, United States)
Karim, M. A.
(Dayton, University OH, United States)
Liu, H. K.
(JPL Pasadena, CA, United States)
Date Acquired
August 14, 2013
Publication Date
January 1, 1989
Subject Category
Cybernetics
Meeting Information
Meeting: Optical Pattern Recognition
Location: Los Angeles, CA
Country: United States
Start Date: January 17, 1989
End Date: January 18, 1989
Accession Number
90A32103
Distribution Limits
Public
Copyright
Other

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