Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data

  • Martin Holeňa


The extraction of logical rules from data is a key application of artificial neural networks (ANNs) in data mining. However, most of the ANN-based rule extraction methods rely primarily on heuristics, and their underlying theoretical principles are not very deep. That is especially much true for methods extracting fuzzy logic rules, which usually allow to mix different logical connectives in such a way that extracted rules can not be correctly evaluated in any particular fuzzy logic model. This paper shows that mixing of connectives is not needed. A method for fuzzy rules extraction for which the evaluation of the extracted rules in a single model is the basic principle is outlined and illustrated on a case study with EEG data.


Fuzzy Logic Fuzzy Rule Logical Connective Disjunctive Normal Form Rule Extraction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag/Wien 2005

Authors and Affiliations

  • Martin Holeňa
    • 1
  1. 1.Institute of Computer ScienceAcademy of Sciences of the Czech RepublicPraha 8

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