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Statements

Subject Item
n2:RIV%2F61989100%3A27240%2F13%3A86092932%21RIV15-MSM-27240___
rdf:type
skos:Concept n18:Vysledek
dcterms:description
A novel learning algorithm is proposed for non linear modeling and identification by the use of the beta basis function neural network (BBFNN). The proposed method is a hierarchical particle swarm optimization (HPSO). The objective of this paper is to optimize the parameters of the beta basis function neural network (BBFNN) with high accuracy. The population of HPSO forms multiple beta neural networks with different structures at an upper hierarchical level and each particle of the previous population is optimized at a lower hierarchical level to improve the performance of each particle swarm. For the beta neural network consisting n particles are formed in the upper level to optimize the structure of the beta neural network. In the lower level, the population within the same length particle is to optimize the free parameters of the beta neural network. Experimental results on a number of benchmarks problems drawn from regression and time series prediction area demonstrate that the HPSO produces a better generalization performance. 2013 Springer-Verlag. A novel learning algorithm is proposed for non linear modeling and identification by the use of the beta basis function neural network (BBFNN). The proposed method is a hierarchical particle swarm optimization (HPSO). The objective of this paper is to optimize the parameters of the beta basis function neural network (BBFNN) with high accuracy. The population of HPSO forms multiple beta neural networks with different structures at an upper hierarchical level and each particle of the previous population is optimized at a lower hierarchical level to improve the performance of each particle swarm. For the beta neural network consisting n particles are formed in the upper level to optimize the structure of the beta neural network. In the lower level, the population within the same length particle is to optimize the free parameters of the beta neural network. Experimental results on a number of benchmarks problems drawn from regression and time series prediction area demonstrate that the HPSO produces a better generalization performance. 2013 Springer-Verlag.
dcterms:title
Hierarchical particle swarm optimization for the design of beta basis function neural network Hierarchical particle swarm optimization for the design of beta basis function neural network
skos:prefLabel
Hierarchical particle swarm optimization for the design of beta basis function neural network Hierarchical particle swarm optimization for the design of beta basis function neural network
skos:notation
RIV/61989100:27240/13:86092932!RIV15-MSM-27240___
n3:aktivita
n16:S
n3:aktivity
S
n3:dodaniDat
n10:2015
n3:domaciTvurceVysledku
Abraham Padath, Ajith
n3:druhVysledku
n11:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n15:predkladatel
n3:idSjednocenehoVysledku
77314
n3:idVysledku
RIV/61989100:27240/13:86092932
n3:jazykVysledku
n9:eng
n3:klicovaSlova
Time series prediction; Particle swarm; Nonlinear modeling; Hierarchical particle swarm optimization; Hierarchical level; Generalization performance; Free parameters; Different structure; Basis functions
n3:klicoveSlovo
n7:Generalization%20performance n7:Free%20parameters n7:Nonlinear%20modeling n7:Different%20structure n7:Basis%20functions n7:Particle%20swarm n7:Time%20series%20prediction n7:Hierarchical%20level n7:Hierarchical%20particle%20swarm%20optimization
n3:kontrolniKodProRIV
[0F843145FF90]
n3:mistoKonaniAkce
Chennai
n3:mistoVydani
Heidelberg
n3:nazevZdroje
Advances in Intelligent Systems and Computing. Volume 182
n3:obor
n4:IN
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n10:2013
n3:tvurceVysledku
Abraham Padath, Ajith Alimi, A. M. Dhahri, H.
n3:typAkce
n17:WRD
n3:zahajeniAkce
2012-08-04+02:00
s:issn
2194-5357
s:numberOfPages
13
n14:doi
10.1007/978-3-642-32063-7_22
n6:hasPublisher
Springer-Verlag
n20:isbn
978-3-642-32062-0
n12:organizacniJednotka
27240