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Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks

SYSNO ASEP	0405527
Document Type	J - Journal Article
R&D Document Type	Journal Article
Subsidiary J	Článek ve WOS
Title	Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks
Title	Extrakce pravidel fuzzy logiky z dat pomocí umělých neuronových sítí
Author(s)	Holeňa, Martin (UIVT-O)_{SAI, RID}
Source Title	Kybernetika. - : Ústav teorie informace a automatizace AV ČR, v. v. i. - ISSN 0023-5954 Roč. 41, č. 3 (2005), s. 297-314
Number of pages	18 s.
Language	eng - English
Country	CZ - Czech Republic
Keywords	knowledge extraction from data ; artificial neural networks ; fuzzy logic ; Lukasiewicz logic ; disjunctive normal form
Subject RIV	BA - General Mathematics
R&D Projects	IAA1030004 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000233665200003
EID SCOPUS	25444464640
Annotation	A method for the extraction of rules in a general fuzzy disjunctive normal form is described in detail and illustrated on real-world applications. Furter, the paper proposes an algorithm demonstrating a principal possibility to extract fuzzy logic rules from multilayer perceptrons with continuous activation functions, i.e., from the kind of neural networks most universally used in applications. However, complexity analysis of the individual steps of that algorithm reveals that it involves computations with doubly-exponential complexity, due to which it can not without simplifications serve as a practically applicable alternative to methods based on specialized neural networks.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2006

Number of the records: 1