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  • In this paper we proposed a fuzzy neural network model which can embody a fuzzy Takagi-Sugeno model and curry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of new origin procedures namely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure identification. In the proposed fuzzy modeling method we first design a rough initial fuzzy model with complete partition of input variable space (or initial partition based on expert knowledge). Then a fuzzy neural network is constructed based on rough fuzzy model. By learning of the neural network we can tune of embedded initial fuzzy model.
  • In this paper we proposed a fuzzy neural network model which can embody a fuzzy Takagi-Sugeno model and curry out fuzzy inference and support structure of fuzzy rules. The algorithm of model properties improvement consists of new origin procedures namely input space partition, fuzzy terms number and rule number extending, low-effective fuzzy terms and rules extraction and consequent structure identification. In the proposed fuzzy modeling method we first design a rough initial fuzzy model with complete partition of input variable space (or initial partition based on expert knowledge). Then a fuzzy neural network is constructed based on rough fuzzy model. By learning of the neural network we can tune of embedded initial fuzzy model. (en)
  • V tomto článku je navržena umělá neuronová síť realizující fuzzy model Takagi-Sugeno. Jsou dále navrženy algoritmy pro strukturální a parametrickou on-line identifikaci T-S fuzzy modelu. V rámci identifikace je nejprve vytvořen hrubý model s úplným inicializačním pokrytím celého prostoru vstupních proměnných. Je navržena nová procedura pro dělení fuzzy prostoru k dosažení, co nejmenší chyby modelu. Posledním krokem je extrakce pravidel s minimálním podílem chyby na celkové chybě modelu. Pro optimální nastavení parametrů těchto procedur je použita strategie genetického algoritmu. Tyto procedury a strategie GA byly implementovány do systému FUZNET a použity pro dvě případové studie: predikci M-G časové řady a pro modelování chlazení koksového plynu. (cs)
Title
  • Parameters optimization of fuzzy-neural dynamic model
  • Parameters optimization of fuzzy-neural dynamic model (en)
  • Optimalizace parametrů fuzzy neuronového dynamického modelu (cs)
skos:prefLabel
  • Parameters optimization of fuzzy-neural dynamic model
  • Parameters optimization of fuzzy-neural dynamic model (en)
  • Optimalizace parametrů fuzzy neuronového dynamického modelu (cs)
skos:notation
  • RIV/47813059:19240/04:#0001894!RIV09-MSM-19240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 578843
http://linked.open...ai/riv/idVysledku
  • RIV/47813059:19240/04:#0001894
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • fuzzy-neural model; Takagi-Sugeno model (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [0B3290B913AA]
http://linked.open...v/mistoKonaniAkce
  • Banff, Canada
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • NAFIPS 2004: ANNUAL MEETING OF THE NORTH AMERICAN FUZZY INFORMATION PROCESSING SOCIETY
http://linked.open...in/vavai/riv/obor
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http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Čermák, Petr
  • Chmiel, Pavel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
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  • IEEE
https://schema.org/isbn
  • 0-7803-8376-1
http://localhost/t...ganizacniJednotka
  • 19240
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