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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 zjednodušení sloužit jako použitelná alternativa k metodám extrakce založeným na specializovaných neuronových sítích. (cs)
  • 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.
  • 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. (en)
Title
  • Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks
  • Extrakce pravidel fuzzy logiky z dat pomocí umělých neuronových sítí (cs)
  • Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks (en)
skos:prefLabel
  • Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks
  • Extrakce pravidel fuzzy logiky z dat pomocí umělých neuronových sítí (cs)
  • Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks (en)
skos:notation
  • RIV/67985807:_____/05:00405527!RIV06-AV0-67985807
http://linked.open.../vavai/riv/strany
  • 297;314
http://linked.open...avai/riv/aktivita
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  • P(IAA1030004), Z(AV0Z10300504)
http://linked.open...iv/cisloPeriodika
  • 3
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http://linked.open...aciTvurceVysledku
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  • 521279
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  • RIV/67985807:_____/05:00405527
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  • knowledge extraction from data; artificial neural networks; fuzzy logic; Lukasiewicz logic; disjunctive normal form (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • CZ - Česká republika
http://linked.open...ontrolniKodProRIV
  • [CB5DD3810F49]
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  • Kybernetika
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http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 41
http://linked.open...iv/tvurceVysledku
  • Holeňa, Martin
http://linked.open...n/vavai/riv/zamer
issn
  • 0023-5954
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