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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 29, Iss. 8 — Mar. 10, 1990
  • pp: 1191–1202

Ho-Kashyap optical associative processors

Brian Telfer and David P. Casasent  »View Author Affiliations


Applied Optics, Vol. 29, Issue 8, pp. 1191-1202 (1990)
http://dx.doi.org/10.1364/AO.29.001191


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Abstract

A Ho-Kashyap (H-K) associative processor (AP) is shown to have a larger storage capacity than the pseudoinverse and correlation APs and to accurately store linearly dependent key vectors. Prior APs have not demonstrated good performance on linearly dependent key vectors. The AP is attractive for optical implementation. A new robust H-K AP is proposed to improve noise performance. These results are demonstrated both theoretically and by Monte Carlo simulation. The H-K AP is also shown to outperform the pseudoinverse AP in an aircraft recognition case study. A technique is developed to indicate the least reliable output vector elements and a new AP error correcting synthesis technique is advanced.

© 1990 Optical Society of America

History
Original Manuscript: May 18, 1989
Published: March 10, 1990

Citation
Brian Telfer and David P. Casasent, "Ho-Kashyap optical associative processors," Appl. Opt. 29, 1191-1202 (1990)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-29-8-1191


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References

  1. G. S. Stiles, D.-L. Denq, “A Quantitative Comparison of the Performance of Three Discrete Distributed Associative Memory Models,” IEEE Trans. Comput. C-36, 257–263 (1987). [CrossRef]
  2. J. Hopfield, “Neural Networks and Physical Systems with Emergent Collective Computational Abilities,” Proc. Natl. Acad. Sci. USA 79, 2554–2558 (1982). [CrossRef] [PubMed]
  3. R. McEliece et al., “The Capacity of the Hopfield Associative Memory,” IEEE Trans. Info. Theory IT-33, 461–482 (1987). [CrossRef]
  4. B. Telfer, D. Casasent, “Ho-Kashyap Associative Processors,” Proc. Soc. Photo-Opt. Instrum. Eng. 1005, 77–87 (1988).
  5. T. Kohonen, Self-Organization and Associative Memory (Springer-Verlag, Berlin, 1987).
  6. G. Stiles, D.-L. Denq, “On the Effect of Noise on the Moore-Penrose Generalized Inverse Associative Memory,” IEEE Trans. Pat. Anal. and Mach. Int. PAMI-7, 358–360 (1985). [CrossRef]
  7. K. Murakami, T. Aibara, “An Improvement on the Moore-Penrose Generalized Inverse Associative Memory,” IEEE Trans. Syst. Man and Cybern. SMC-17, 699–707 (1987). [CrossRef]
  8. D. Casasent, B. Telfer, “Key and Recollection Vector Effects on Heteroassociative Memory Performance,” Appl. Opt. 28, 272–283 (1989). [CrossRef] [PubMed]
  9. Y.-C. Ho, R. Kashyap, “An Algorithm for Linear Inequalities and Its Applications,” IEEE Trans. Electron. Comput. EC-14, 683–688 (1965). [CrossRef]
  10. T. Cover, “Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition,” IEEE Trans. Electron. Comput. EC-14, 326–334 (1965). [CrossRef]
  11. M. Hassoun, “Two-Level Neural Network for Deterministic Logic Processing,” Proc. Soc. Photo-Opt. Instrum. Eng. 881, 258–264 (1988).
  12. M. Hassoun, D. Clark, “An Adaptive Attentive Learning Algorithm for Single-Layer Neural Networks,” IEEE Int. Conf. Neural Networks I431–440 (1988). [CrossRef]
  13. M. Hassoun, “A High-Performance Associative Neural Memory (ANM) for Pattern Recognition,” Proc. Soc. Photo-Opt. Instrum. Eng. 956 (1988).
  14. M. Hassoun, A. Youssef, “High Performance Recording Algorithm for Hopfield Model Associative Memories,” Opt. Eng. 28, 46–54 (1989). [CrossRef]
  15. M. H. Hassoun, “Adaptive Dynamic Heteroassociative Neural Memories for Pattern Classification,” Proc. Soc. Photo-Opt. Instrum. Eng. 1053, 75–83 (1989).
  16. A. M. Youssef, M. H. Hassoun, “Dynamic Autoassociative Neural Memory Performance vs. Capacity,” Proc. Soc. Photo-Opt. Instrum. Eng. 1053, 52–59 (1989).
  17. B. Kosko, “Bidirectional Associative Memories,” IEEE Trans. Syst. Man and Cybern. SMC-18, 49–60 (1988). [CrossRef]
  18. B. L. Montgomery, B. V. K. Vijaya Kumar, “An Evaluation of the Use of the Hopfield Neural Network Model as a Nearest-Neighbor Algorithm,” Appl. Opt. 25, 3759–3766 (1986). [CrossRef] [PubMed]
  19. R. P. Lippmann, “An Introduction to Computing with Neural Nets,” IEEE ASSP Mag. 4, 4–22 (1987). [CrossRef]
  20. A. D. Fisher, W. L. Lippincott, J. W. Lee, “Optical Implementations of Associative Networks with Versatile Adaptive Learning Capabilities,” Appl. Opt. 26, 5039–5054 (1987). [CrossRef] [PubMed]
  21. G. Strang, Linear Algebra and Its Applications (Harcourt, Brace, Jovanovich, New York, 1980).
  22. R. Duda, P. Hart, Pattern Classification and Scene Analysis (Wiley, New York, 1973).
  23. Y.-C. Ho, R. Kashyap, “A Class of Iterative Procedures for Linear Inequalities,” J. SIAM Control 4, 112–115 (1966). [CrossRef]
  24. J. Goodman et al., “Parallel Incoherent Optical Vector-Matrix Multiplier,” Technical Report L-723-1, BMD (1979).
  25. D. Casasent, J. Jackson, C. Neuman, “Frequency-Multiplexed and Pipelined Iterative Optical Systolic Array Processors,” Appl. Opt. 22, 115–124 (1983). [CrossRef] [PubMed]
  26. D. Casasent, J. Jackson, “Space and Frequency-Multiplexed Optical Linear Algebra Processor: Fabrication and Initial Tests,” Appl. Opt. 25, 2258–2263 (1986). [CrossRef] [PubMed]
  27. K. Wagner, D. Psaltis, “A Space Integrating Acousto-Optic Matrix-Matrix Multiplier,” Opt. Commun. 52, 173–177 (1984). [CrossRef]
  28. R. Winder, “Bounds on Threshold Gate Realizability,” IEEE Trans. Electron. Comput. EC-12, 561–564 (1963). [CrossRef]
  29. C. Giles, T. Maxwell, “Learning, Invariance, and Generalization in High-Order Neural Networks,” Appl. Opt. 26, 4972–4978 (1987). [CrossRef] [PubMed]
  30. D. Psaltis, C. Park, J. Hong, “Higher Order Associative Memories and Their Optical Implementations,” Neural Networks 1, 149–163 (1988). [CrossRef]
  31. Y. Kosugi, Y. Naito, “An Associative Memory as a Model for the Cerebellar Cortex,” IEEE Trans. Syst. Man Cybern. SMC-7, 95–98 (1977).
  32. H. Kasden, “Industrial Applications of Diffraction Pattern Sampling,” Opt. Eng. 18, 496–503 (1979).

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