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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F14%3A86092753%21RIV15-MSM-27240___
rdf:type
skos:Concept n20:Vysledek
dcterms:description
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.
dcterms:title
Multi-agent evolutionary design of Beta fuzzy systems Multi-agent evolutionary design of Beta fuzzy systems
skos:prefLabel
Multi-agent evolutionary design of Beta fuzzy systems Multi-agent evolutionary design of Beta fuzzy systems
skos:notation
RIV/61989100:27240/14:86092753!RIV15-MSM-27240___
n3:aktivita
n19:S
n3:aktivity
S
n3:dodaniDat
n10:2015
n3:domaciTvurceVysledku
Abraham Padath, Ajith
n3:druhVysledku
n4:D
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n14:predkladatel
n3:idSjednocenehoVysledku
30924
n3:idVysledku
RIV/61989100:27240/14:86092753
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Particle swarm optimization (PSO); Membership functions; Learning algorithms; Knowledge based systems; Fuzzy systems; Design
n3:klicoveSlovo
n9:Design n9:Knowledge%20based%20systems n9:Fuzzy%20systems n9:Particle%20swarm%20optimization%20%28PSO%29 n9:Learning%20algorithms n9:Membership%20functions
n3:kontrolniKodProRIV
[6BE82683CFA7]
n3:mistoKonaniAkce
Beijing
n3:mistoVydani
New York
n3:nazevZdroje
IEEE International Conference on Fuzzy Systems
n3:obor
n8:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
4
n3:rokUplatneniVysledku
n10:2014
n3:tvurceVysledku
Abraham Padath, Ajith Alimi, A. M. Bouaziz, S. Jarraya, Y.
n3:typAkce
n13:WRD
n3:zahajeniAkce
2014-07-06+02:00
s:issn
1098-7584
s:numberOfPages
8
n11:doi
10.1109/FUZZ-IEEE.2014.6891722
n16:hasPublisher
Institute of Electrical and Electronics Engineers
n18:isbn
978-1-4799-2072-3
n12:organizacniJednotka
27240