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Symbolical Reasoning about Numerical Data: A Hybrid Approach

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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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  1. Th Darmstadt, FB Informatik, FG Intellektik, Alexanderstr. 10, 64283 Darmstadt, Germany. E-mail

    Christoph S. Herrmann

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  1. Christoph S. Herrmann
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Herrmann, C.S. Symbolical Reasoning about Numerical Data: A Hybrid Approach. Applied Intelligence 7, 339–354 (1997). https://doi.org/10.1023/A:1008217621798

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