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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Vol. 26, Iss. 23 — Dec. 1, 1987
  • pp: 4979–4984

Counterpropagation networks

Robert Hecht-Nielsen  »View Author Affiliations


Applied Optics, Vol. 26, Issue 23, pp. 4979-4984 (1987)
http://dx.doi.org/10.1364/AO.26.004979


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Abstract

By combining Kohonen learning and Grossberg learning a new type of mapping neural network is obtained. This counterpropagation network (CPN) functions as a statistically optimal self-programming lookup table. The paper begins with some introductory comments, followed by the definition of the CPN. Then a closed-form formula for the error of the network is developed. The paper concludes with a discussion of CPN variants and comments about CPN convergence and performance. References and a neurocomputing bibliography with a combined total of eighty entries are provided.

© 1987 Optical Society of America

History
Original Manuscript: July 18, 1987
Published: December 1, 1987

Citation
Robert Hecht-Nielsen, "Counterpropagation networks," Appl. Opt. 26, 4979-4984 (1987)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-26-23-4979


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References

  1. D. E. Rumelhart, J. L. McClelland, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vols. 1, 2, 3 (MIT Press, Cambridge, MA, 1986, 1987).
  2. T. Kohonen, Self-Organization and Associative Memory (Springer-Verlag, New York, 1984).
  3. S. Grossberg, Studies of Mind and Brain (Reidel, Boston, MA, 1982). [CrossRef]
  4. R. Hecht-Nielsen, “Combinatorial Hypercompression,” in Proceedings, IEEE International Conference on Neural Networks (IEEE, New York, 1987).

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