Encyclopedia of Operations Research and Management Science

2001 Edition
| Editors: Saul I. Gass, Carl M. Harris

Neural networks

Reference work entry
DOI: https://doi.org/10.1007/1-4020-0611-X_668
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INTRODUCTION

While the early research in neural network models of intelligence surfaced several decades ago, the field nearly died in the 1960s due to obstacles which seemed insurmountable. Critical advances in the late 1970s and early 1980s, however, led to a resurgence of interest and activity in the area (Hopfield 1984; Rumelhart, Hinton and Williams 1986). The classical model of human cellular activity, proposed by McCulloch and Pitts (1943), still forms the foundation of much of the work being conducted in current neural network research.

A real neural network is that interconnection of elements within the mammalian brain, and those activities that go on within and between these elements, that evidently serve to carry out the decision making process (e.g., memory, recognition, prediction, planning, problem solving). Artificial neural networks represent mankind's rather feeble attempts to replicate such biological processes by means of algorithms in conjunction with either physical...

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References

  1. [1]
    Burke, L.I. and Ignizio, J.P. (1992). “Neural Networks and Operations Research: An Overview,” Computers and Operations Research, 19, 179–189.Google Scholar
  2. [2]
    Burke, L.I. (1991). “Introduction to Artificial Neural Systems for Pattern Recognition,” Computers and Operations Research, 18, 211–220.Google Scholar
  3. [3]
    Haykin, S. (1999). Neural Networks. Pirentice-Hall, Upper Saddle River, New Jersey.Google Scholar
  4. [4]
    Lippmann, R.P. (1987). “An Introduction to Computing with Neural Networks,” IEEE ASSP Magazine, April, 4–22. Google Scholar
  5. [5]
    McCulloch, W.S. and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity.” Bulletin Mathematical Biophysics, 5, 115–133.Google Scholar
  6. [6]
    Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). “Learning representations by back-propagating errors.” Nature, 323, 533–536.Google Scholar
  7. [7]
    Hopfield, J.J. (1984). “Neurons with graded response have collective computational properties like those of two-state neurons.” Proceedings National Academy of Sciences, 81, 3088–3092.Google Scholar
  8. [8]
    Hopfield, J.J. and Tank, D.W. (1985). “Neural Computation of Decisions in Optimization Problems.” Biological Cybernetics, 52, 141–152.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  1. 1.University of VirginiaCharlottesvilleUSA
  2. 2.Lehigh UniversityBethlehemUSA