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Optical Engineering

Machine Parts Recognition Using A Trinary Associative Memory
Author(s): Abdul Ahad S. Awwal; Mohammad A Karim; Hua-Kuang Liu
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Paper Abstract

The convergence mechanism of vectors in Hopfield's neural network in relation to recognition of partially known patterns is studied in terms of both inner products and Hamming distance. It has been shown that Hamming distance should not always be used in determining the convergence of vectors. Instead, inner product weighting coefficients play a more dominant role in certain data representations for determining the convergence mechanism. A trinary neuron representation for associative memory is found to be more effective for associative recall. Applications of the trinary associative memory to reconstruct machine part images that are partially missing are demonstrated by means of computer simulation as examples of the usefulness of this approach.

Paper Details

Date Published: 1 May 1989
PDF: 7 pages
Opt. Eng. 28(5) 285537 doi: 10.1117/12.7976994
Published in: Optical Engineering Volume 28, Issue 5
Show Author Affiliations
Abdul Ahad S. Awwal, The University of Dayton (United States)
Mohammad A Karim, The University of Dayton (United States)
Hua-Kuang Liu, Jet Propulsion Laboratory (United States)

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