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dc.contributor.authorGoedtel, A.-
dc.contributor.authorda Silva, I. N.-
dc.contributor.authorSerni, PJA-
dc.contributor.authorieee-
dc.date.accessioned2014-05-20T15:25:49Z-
dc.date.accessioned2016-10-25T18:00:23Z-
dc.date.available2014-05-20T15:25:49Z-
dc.date.available2016-10-25T18:00:23Z-
dc.date.issued2004-01-01-
dc.identifierhttp://dx.doi.org/10.1109/IJCNN.2004.1380074-
dc.identifier.citation2004 IEEE International Joint Conference on Neural Networks, Vols 1-4, Proceedings. New York: IEEE, p. 1021-1026, 2004.-
dc.identifier.issn1098-7576-
dc.identifier.urihttp://hdl.handle.net/11449/36159-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/36159-
dc.description.abstractThe multilayer perceptron network has become one of the most used in the solution of a wide variety of problems. The training process is based on the supervised method where the inputs are presented to the neural network and the output is compared with a desired value. However, the algorithm presents convergence problems when the desired output of the network has small slope in the discrete time samples or the output is a quasi-constant value. The proposal of this paper is presenting an alternative approach to solve this convergence problem with a pre-conditioning method of the desired output data set before the training process and a post-conditioning when the generalization results are obtained. Simulations results are presented in order to validate the proposed approach.en
dc.format.extent1021-1026-
dc.language.isoeng-
dc.publisherIEEE-
dc.sourceWeb of Science-
dc.titleAn alternative approach to solve convergence problems in the backpropagation algorithmen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationState Univ São Paulo, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil-
dc.description.affiliationUnespState Univ São Paulo, Dept Elect Engn, BR-15385000 Ilha Solteira, SP, Brazil-
dc.identifier.doi10.1109/IJCNN.2004.1380074-
dc.identifier.wosWOS:000224941900177-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartof2004 IEEE International Joint Conference on Neural Networks, Vols 1-4, Proceedings-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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