Backpropagation-Reference-Cited by-同舟云学术

Backpropagation

Published:2001-12-01 Issue:9/10 Volume:30 Page:1110-1117
ISSN:0368-492X
Container-title:Kybernetes
language:en
Short-container-title:

Author:

Andrew Alex M.

Abstract

The popular backpropagation algorithm for training neural nets is a special case of an earlier principle of significance feedback, which in turn has much in common with Selfridge’s “Pandemonium” and a connection with McCulloch’s “redundancy of potential command”. Ways in which the effects might operate in real neural nets are reviewed, and the ideas are related to the current interest in heterogeneous agents. The tendency to restrict attention to numerical optimisation is regretted.

Publisher

Emerald

Subject

Computer Science (miscellaneous),Social Sciences (miscellaneous),Theoretical Computer Science,Control and Systems Engineering,Engineering (miscellaneous)

Reference18 articles.

1. Andrew, A.M. (1965), Significance Feedback in Neural Nets, Report of Biological Computer Laboratory, University of Illinois, Urbana, IL.

2. Andrew, A.M. (1967), “To model or not to model”, Kybernetik, Vol. 3 Part 6, pp. 272‐5.

3. Andrew, A.M. (1973), ‘‘Significance feedback and redundancy reduction in self‐organizing networks”, in Pichler, F. and Trappl, R. (Eds), Advances in Cybernetics and Systems Research 1, Hemisphere, London, pp. 244‐52.

4. Andrew, A.M. (1993, “Significance feedback in neural nets” (reprint of the 1965 paper with additional notes), Int. J. Systems Research and Info. Science, Vol. 6, pp. 59‐67.

5. Andrew, A.M. (2000), “Self‐organisation in artificial neural nets”, Kybernetes, Vol. 29 No. 5, pp. 638‐50.

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