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  Symbolical reasoning about numerical data: A hybrid approach

Herrmann, C. S. (1997). Symbolical reasoning about numerical data: A hybrid approach. Applied Intelligence, 7(4), 339-354. doi:10.1023/A:1008217621798.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0003-3A30-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0003-3A31-5
Genre: Journal Article

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 Creators:
Herrmann, Christoph S.1, Author              
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1External Organizations, ou_persistent22              

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Free keywords: expert systems; fuzzy logic; hybrid systems; medical diagnosis; neural networks
 Abstract: By combining methods from artificial intelligence and signal analysis, we have developed a hybrid system for medical diagnosis. The core of the system is a fuzzy expert system with a dual source knowledge base. Two sets of rules are acquired, automatically from given examples and indirectly formulated by the physician. A fuzzy neural network serves to learn from sample data and allows to extract fuzzy rules for the knowledge base. A complex signal transformation preprocesses the digital data a priori to the symbolic representation. Results demonstrate the high accuracy of the system in the field of diagnosing electroencephalograms where it outperforms the visual diagnosis by a human expert for some phenomena.

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Language(s): eng - English
 Dates: 1997-11
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1023/A:1008217621798
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Title: Applied Intelligence
Source Genre: Journal
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Pages: - Volume / Issue: 7 (4) Sequence Number: - Start / End Page: 339 - 354 Identifier: -