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

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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.

Item Type: Article
Subjects: Computing > Artificial Intelligence
Divisions: Faculty of Technology
Faculty of Technology > School of Computer Science
Related URLs:
Depositing User: Kenneth McGarry
Date Deposited: 17 Apr 2015 08:10
Last Modified: 18 Dec 2019 15:37
URI: http://sure.sunderland.ac.uk/id/eprint/5334
ORCID for Kenneth McGarry: ORCID iD orcid.org/0000-0002-9329-9835

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