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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Hybrid neural systems: from simple coupling to fully integrated neural networks

McGarry, Kenneth, Wermter, Stefan and MacIntyre, John (1999) Hybrid neural systems: from simple coupling to fully integrated neural networks. Neural Computing Surveys, 2 (1). pp. 62-93. ISSN 1093-7609

Item Type: Article


This paper describes techniques for integrating neural networks and symbolic components into powerful hybrid systems. Neural networks have unique processing characteristics that enable tasks to be performed that would be di cult or intractable for a symbolic rule-based system. However, a stand-alone neural network requires an interpretation either by a human or a rule based system. This motivates the integration of neural/symbolic techniques within a hybrid system. A number of integration possibilities exist: some systems consist of neural network components performing symbolic tasks while other systems are composed of several neural networks and symbolic components, each component acting as a self-contained module communicating with the others. Other hybrid systems are able to transform subsymbolic representations into symbolic ones and vice-versa. This paper provides an overview and evaluation of the state of the art of several hybrid neural systems for rule-based processing.

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Depositing User: Kenneth McGarry


Item ID: 5334
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ISSN: 1093-7609
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ORCID for Kenneth McGarry: ORCID iD

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Date Deposited: 17 Apr 2015 08:10
Last Modified: 18 Dec 2019 15:37


Author: Kenneth McGarry ORCID iD
Author: Stefan Wermter
Author: John MacIntyre

University Divisions

Faculty of Technology
Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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