Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/101248
Type: Artigo de evento
Title: Rbf Neural Networks With Centers Assignment Via Karhunen-loeve Transform
Author: De Castro Maria C.F.
De Castro Fernando C.C.
Arantes Dalton S.
Abstract: A technique for assigning the gaussian centers to a Radial Basis Function Neural Network (RBFNN), based on the Karhunen-Loeve Transform (KLT), is proposed. By applying this technique to time series prediction, a significant performance improvement is obtained in comparison with usual prediction methods that use RBFNNs. For instance, by assigning the KLT scaled eigenvectors to the RBFNN centers yields lower prediction normalized mean squared error (NMSE) and requires less previous known samples than the usual technique that applies the own training set vectors to the centers. The present technique has also shown improved performance when compared with prediction based on RBFNNs that uses the K-means clustering algorithm.
Editor: IEEE, United States
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-0033334289&partnerID=40&md5=f6ef3b82245d525e74506ecb44145e23
Date Issue: 1999
Appears in Collections:Unicamp - Artigos e Outros Documentos

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