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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 25, Iss. 20 — Oct. 15, 1986
  • pp: 3759–3766

Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm

Bruce L. Montgomery and B. V. K. Vijaya Kumar  »View Author Affiliations


Applied Optics, Vol. 25, Issue 20, pp. 3759-3766 (1986)
http://dx.doi.org/10.1364/AO.25.003759


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Abstract

Neural network models are receiving increasing attention because of their collective computational capabilities. We evaluate the use of the Hopfield neural network model in optically determining the nearest-neighbor of a binary bipolar test vector from a set of binary bipolar reference vectors. The use of the Hopfield model is compared with that of a direct technique called direct storage nearest-neighbor that accomplishes the task of nearest-neighbor determination.

© 1986 Optical Society of America

History
Original Manuscript: February 20, 1986
Published: October 15, 1986

Citation
Bruce L. Montgomery and B. V. K. Vijaya Kumar, "Evaluation of the use of the Hopfield neural network model as a nearest-neighbor algorithm," Appl. Opt. 25, 3759-3766 (1986)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-25-20-3759


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References

  1. J. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proc. Natl. Acad. Sci. USA 79, 2554 (1982). [CrossRef] [PubMed]
  2. D. Psaltis, N. Farhat, “Optical Information Processing Based on an Associative-Memory Model for Neural Nets with Thresholding and Feedback,” Opt. Lett. 10, 98 (1985). [CrossRef] [PubMed]
  3. N. Farhat, D. Psaltis, A. Prata, E. Paek, “Optical Implementation of the Hopfield Model,” Appl. Opt. 24, 1469 (1985). [CrossRef] [PubMed]
  4. Workshop on Neural Network Models for Computing, Santa Barbara, CA, 30 Apr.–2 May 1985.
  5. Conference on Neural Networks for Computing, Snowbird, UT, 13–16 Apr. 1986.
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  8. R. J. McEliece, E. C. Posner, E. R. Rodemich, S. S. Venkatesh, “The Capacity of the Hopfield Associative Memory,” submitted to IEEE Trans. Inf. Theory (1986).
  9. R. J. McEliece, E. C. Posner, “The Number of Stable Points of an Infinite-Range Spin Glass Memory,” in Telecommunications and Data Acquisition Progress Report, Vol. 42–83, July–Sept. 1985, (Jet Propulsion Laboratory, California Institute of Technology, Pasadena, 15Nov.1985), pp. 209–215.
  10. R. O. Duda, P. E. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).
  11. R. J. McEliece, The Theory of Information and Coding (Addison-Wesley, Reading, MA, 1977), Chap. 7.
  12. L. O’Gorman, A. C. Sanderson, “The Converging Squares Algorithm: an Efficient Method for Locating Peaks in Multidimensions,” IEEE Tran. Pattern. Anal. Machine. Intell. PAMI-6, 280 (1984). [CrossRef]
  13. A. W. Lohmann, W. Stork, G. Stucke, “Optical Perfect Shuffle,” Appl. Opt. 25, 1530 (1986). [CrossRef] [PubMed]

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