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  • This paper provides an overview on a new evolutionary approach based on an intelligent multi-agent architecture to design Beta fuzzy systems (BFSs). The Methodology consists of two processes, a learning process using a clustering technique for the automated design of an initial Beta fuzzy system, and a multi-agent tuning process based on Particle Swarm Optimization algorithm to deal with the optimization of membership functions parameters and rule base. In this approach, dynamic agents use communication and interaction concepts to generate high-performance fuzzy systems. Experiments on several data sets were performed to show the effectiveness of the proposed method in terms of accuracy and convergence speed.
  • This paper provides an overview on a new evolutionary approach based on an intelligent multi-agent architecture to design Beta fuzzy systems (BFSs). The Methodology consists of two processes, a learning process using a clustering technique for the automated design of an initial Beta fuzzy system, and a multi-agent tuning process based on Particle Swarm Optimization algorithm to deal with the optimization of membership functions parameters and rule base. In this approach, dynamic agents use communication and interaction concepts to generate high-performance fuzzy systems. Experiments on several data sets were performed to show the effectiveness of the proposed method in terms of accuracy and convergence speed. (en)
Title
  • Multi-agent evolutionary design of Beta fuzzy systems
  • Multi-agent evolutionary design of Beta fuzzy systems (en)
skos:prefLabel
  • Multi-agent evolutionary design of Beta fuzzy systems
  • Multi-agent evolutionary design of Beta fuzzy systems (en)
skos:notation
  • RIV/61989100:27240/14:86092753!RIV15-MSM-27240___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 30924
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27240/14:86092753
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Particle swarm optimization (PSO); Membership functions; Learning algorithms; Knowledge based systems; Fuzzy systems; Design (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [6BE82683CFA7]
http://linked.open...v/mistoKonaniAkce
  • Beijing
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • IEEE International Conference on Fuzzy Systems
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Abraham Padath, Ajith
  • Alimi, A. M.
  • Bouaziz, S.
  • Jarraya, Y.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 1098-7584
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/FUZZ-IEEE.2014.6891722
http://purl.org/ne...btex#hasPublisher
  • Institute of Electrical and Electronics Engineers
https://schema.org/isbn
  • 978-1-4799-2072-3
http://localhost/t...ganizacniJednotka
  • 27240
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