Help Privacy Policy Disclaimer
  Advanced SearchBrowse




Journal Article

Symbolical reasoning about numerical data: A hybrid approach

There are no MPG-Authors in the publication available
External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available

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

Cite as: https://hdl.handle.net/21.11116/0000-0003-3A30-6
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.