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Neural computing and stochastic optimization

  • Eugene Wong
V. Signal Processing, Control, and Manufacturing Automation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 653)

Abstract

The potential of neural networks to find global optimum should be further explored. Their ability to do so using only local “gradient” information is surprising and can lead to very useful applications. Implementing an optimum receiver-decoder is a particularly interesting example.

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References

  1. [HOP82]
    J. J. Hopfield, “Neural networks and physical systems with emerging collective computational abilities,” Proc. National Academy of Sciences 79 (1982) 2554–2558.CrossRefMathSciNetGoogle Scholar
  2. [KIR83]
    S. Kirkpatrick, C. D. Gelatt, Jr. and M. P. Vecchi, “Optimization by simulated annealing,” Science 220 (1983) 671–680.CrossRefMathSciNetGoogle Scholar
  3. [WON91a]
    E. Wong, “Stochastic neural networks,” Algorithmica 6 (1991) 466–478.MATHCrossRefMathSciNetGoogle Scholar
  4. [WON91b]
    E. Wong, “Implementing Boltzmann machines,” in Stochastic Analysis, E. Mayer-Wolf, E. Merzbach and A. Schwartz, eds., Academic Press, San Diego, 1991.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1992

Authors and Affiliations

  • Eugene Wong
    • 1
    • 2
  1. 1.Office of Science and Technology Policy, the White HouseUSA
  2. 2.University of California at BerkeleyUSA

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