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Proceedings Paper

Neural network training using the bimodal optical computer
Author(s): Mustafa A. G. Abushagur; Anwar M. Helaly; H. John Caulfield
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Paper Abstract

Using the bimodal optical computer for training a hetroassociative memory of a neural network is introduced. The storage capacity of the trained hetroassociative memory is shown to be much higher than that for the Hopefield model. A comparison with the pseudoinverse model shows that in the proposed method the vector recall accuracy is higher when the number of vectors is greater than their size. This method has the potential of being faster than the other methods because of its parallel processing nature. I.

Paper Details

Date Published: 1 August 1990
PDF: 7 pages
Proc. SPIE 1294, Applications of Artificial Neural Networks, (1 August 1990); doi: 10.1117/12.21158
Show Author Affiliations
Mustafa A. G. Abushagur, Univ. of Alabama in Huntsville (United States)
Anwar M. Helaly, Univ. of Alabama in Huntsville (United States)
H. John Caulfield, Univ. of Alabama in Huntsville (United States)


Published in SPIE Proceedings Vol. 1294:
Applications of Artificial Neural Networks
Steven K. Rogers, Editor(s)

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