BeeRBF: A bee-inspired data clustering approach to design RBF neural network classifiers

Neurocomputing

Volumn 172, Issue , 2016, Pages 427-437

  Ferreira Cruz, Dávila Patrícia ^a   Dourado Maia, Renato ^b   da Silva, Leandro Augusto ^a   de Castro, Leandro Nunes ^a  

^a MACKENZIE PRESBYTERIAN UNIVERSITY   (Brazil)

^b STATE UNIVERSITY OF MONTES CLAROS   (Brazil)

Author keywords

Bee inspired algorithms; Data classification; Neural network; Optimal data clustering; RBF

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; FUNCTIONS; NEURAL NETWORKS;

BEE-INSPIRED ALGORITHMS; CLASSIFICATION ACCURACY; DATA CLASSIFICATION; NUMBER OF CENTERS; OPTIMAL DATA; RADIAL BASIS FUNCTION NEURAL NETWORKS; RBF; RBF NEURAL NETWORK;

RADIAL BASIS FUNCTION NETWORKS;

ACCURACY; ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BEERBF APPROACH; CLASSIFICATION ALGORITHM; CLUSTER ANALYSIS; COPTBEES ALGORITHM; INTERMETHOD COMPARISON; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PRIORITY JOURNAL; PROCESS DEVELOPMENT; QUALITY CONTROL; RADIAL BASIS FUNCTION NEURAL NETWORK;

EID: 84946496938     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2015.03.106     Document Type: Article

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