Extraction of Rules from Data using Piecewise-Linear Neural Networks

0404711 - UIVT-O 20020062 RIV TR eng C - Konferenční příspěvek (zahraniční konf.)
Holeňa, Martin
Extraction of Rules from Data using Piecewise-Linear Neural Networks.
Fuzzy Systems and Soft Copmutational Intelligence in Management and Industrial Engineering. Istanbul: ITU Management Science Fakulty, 2002, s. 1-8. ISBN 975-97963-0-9.
[FSSCTIMIE'02. Istanbul (TR), 29.05.2002-31.05.2002]
Grant CEP: GA AV ČR IAB2030007
Výzkumný záměr: AV0Z1030915
Klíčová slova: knowledge extraction with artificial neural networks * Boolean rules * fuzzy rules * multilayer perceptron * piecewise-linear activation function * polyhedra and pseudopolyhedra * Lukasiewicz predicate calculus * rational McNaughton function
Kód oboru RIV: BA - Obecná matematika

The extraction of logical rules from data by means of artificial neural networks is receiving increasingly much attention. The meaning the extracted rules may convey is primarily determined by the set of their possible truth values, according to which two basic kinds of rules can be differentiated - Boolean and fuzzy. This paper presents a mathematically well founded approach based on piecewise-linear activation functions, which is suitable for the extraction of both kinds of rules.
Trvalý link: http://hdl.handle.net/11104/0124950