1998
DOI: 10.1109/72.668896
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Comparative analysis of fuzzy ART and ART-2A network clustering performance

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Cited by 105 publications
(55 citation statements)
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“…The input patterns are to be well aligned and normalized while presenting to such type of MLP leading to no built in variance with respect to local distortions and translations [20]. Various neural network classifiers are explained in [18,[21][22][23][24][25][26]. A convolutional neural network(CNN) tries to solve the problems of MLP by extracting local features and combining them subsequently to perform the detection or recognition.…”
Section: Introductionmentioning
confidence: 99%
“…The input patterns are to be well aligned and normalized while presenting to such type of MLP leading to no built in variance with respect to local distortions and translations [20]. Various neural network classifiers are explained in [18,[21][22][23][24][25][26]. A convolutional neural network(CNN) tries to solve the problems of MLP by extracting local features and combining them subsequently to perform the detection or recognition.…”
Section: Introductionmentioning
confidence: 99%
“…Além disso, este modelo de RNA foi desenvolvido baseando-se no modelo ART1, mas adaptado ao tratamento de valores reais pertencentes ao intervalo [0, 1] (i.e, valores fuzzy) (Carpenter et al, 1991b). Mais detalhes a respeito deste modelo também podem ser encontrados nos trabalhos de (Frank et al, 1998) e (Baraldi and Bionda, 1999).…”
Section: Fuzzy Artunclassified
“…O algoritmo segue avaliando se o neurõnio vencedor, em F2, é suficientemente similar ao padrão de entrada x; se for verdadeiro, a rede entra em processo de atualização de pesos (ressonância). (Carpenter et al, 1991b) (Frank et al, 1998).…”
Section: Descrição Do Treinamento Da Rede Fuzzy Artunclassified
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