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Self‐organisation in artificial neural nets

Self‐organisation in artificial neural nets The usefulness of artificial neural nets stems from their ability to self‐adjust, or in some sense “learn”. In modern studies, the emphasis on powerful self‐organisation is less strong, but the early viewpoint is defended here as potentially useful. Possible extension of neural net capability to “symbolic” processing is related to Minsky’s “heuristic connection” and to Pask’s view of learning as necessarily involving reformulation of information in a new language. Relevance is demonstrated to the “Boxes” scheme of Michie and Chambers and recent developments in reinforcement learning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Kybernetes Emerald Publishing

Self‐organisation in artificial neural nets

Kybernetes , Volume 29 (5/6): 13 – Jul 1, 2000

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Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0368-492X
DOI
10.1108/03684920010333099
Publisher site
See Article on Publisher Site

Abstract

The usefulness of artificial neural nets stems from their ability to self‐adjust, or in some sense “learn”. In modern studies, the emphasis on powerful self‐organisation is less strong, but the early viewpoint is defended here as potentially useful. Possible extension of neural net capability to “symbolic” processing is related to Minsky’s “heuristic connection” and to Pask’s view of learning as necessarily involving reformulation of information in a new language. Relevance is demonstrated to the “Boxes” scheme of Michie and Chambers and recent developments in reinforcement learning.

Journal

KybernetesEmerald Publishing

Published: Jul 1, 2000

Keywords: Artificial neural networks; Cybernetics; Learning; Fractals; Topology

References