Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks
Result description
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 rulesfrom 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.
Keywords
knowledge extraction from dataartificial neural networksfuzzy logicLukasiewicz logicdisjunctive normal form
The result's identifiers
Result code in IS VaVaI
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks
Original language description
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 rulesfrom 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.
Czech name
Extrakce pravidel fuzzy logiky z dat pomocí umělých neuronových sítí
Czech description
Detailně je diskutována a na reálných datech ilustrována metoda pro extrakci pravidel v obecné fuzzy disjunktivní normální formě. Dále článek navrhuje algoritmus demonstrující principiální možnost extrakce pravidel fuzzy logiky z vícevrstvých perceptronů, tj. onoho typu neuronových sítí, který se v aplikacích používá nejuniverzálněji. Avšak analýza komplexity jednotlivých kroků tohoto algoritmu ukazuje, že zahrnuje výpočty s dvojitě exponenciální přesností, díky čemuž algoritmus nemůže bez zjednodušenísloužit jako použitelná alternativa k metodám extrakce založeným na specializovaných neuronových sítích.
Classification
Type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BA - General mathematics
OECD FORD branch
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Result continuities
Project
IAA1030004: Mathematical foundations of inference under vagueness and uncertainty
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2005
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Kybernetika
ISSN
0023-5954
e-ISSN
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Volume of the periodical
41
Issue of the periodical within the volume
3
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
18
Pages from-to
297-314
UT code for WoS article
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EID of the result in the Scopus database
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Basic information
Result type
Jx - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP
BA - General mathematics
Year of implementation
2005