A bee-inspired data clustering approach to design RBF neural network classifiers
Tipo
Artigo de evento
Data de publicação
2014
Periódico
Advances in Intelligent Systems and Computing
Citações (Scopus)
3
Autores
Cruz D.P.F.
da Silva L.A.
de Castro L.N.
Maia R.D.
da Silva L.A.
de Castro L.N.
Maia R.D.
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Resumo
Different methods have been used to train radial basis function neural networks. This paper proposes a bee-inspired algorithm to automatically select the number and location of basis functions to be used in such RBF network. The algorithm was designed to solve data clustering problems, where the centroids of clusters are used as centers for the RBF network. The approach presented in this paper is preliminary evaluated in three synthetic datasets, two classification datasets and one function approximation problem, and its results suggest a potential for real-world application. © Springer International Publishing Switzerland 2014.
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Assuntos Scopus
Bee-inspired algorithms , Classification datasets , Function approximation problems , Optimal data , Radial basis function neural networks , RBF , RBF Neural Network , Synthetic datasets