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

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F05%3A00405494" target="_blank" >RIV/67985807:_____/05:00405494 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Neural Networks for Extraction of Fuzzy Logic Rules with Application to EEG Data
Original language description
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.
Czech name
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Czech description
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Project
<a href="/en/project/ME%20701" target="_blank" >ME 701: Creation of neuro-information database and mining informations from this database</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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