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Please use this identifier to cite or link to this item: http://acervodigital.unesp.br/handle/11449/70591
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dc.contributor.authorLucks, Marcio B.-
dc.contributor.authorNobuo, Oki-
dc.date.accessioned2014-05-27T11:23:40Z-
dc.date.accessioned2016-10-25T18:26:02Z-
dc.date.available2014-05-27T11:23:40Z-
dc.date.available2016-10-25T18:26:02Z-
dc.date.issued2008-09-30-
dc.identifierhttp://dx.doi.org/10.1109/CIMSA.2008.4595826-
dc.identifier.citationCIMSA 2008 - IEEE Conference on Computational Intelligence for Measurement Systems and Applications Proceedings, p. 23-27.-
dc.identifier.urihttp://hdl.handle.net/11449/70591-
dc.identifier.urihttp://acervodigital.unesp.br/handle/11449/70591-
dc.description.abstractA RBFN implemented with quantized parameters is proposed and the relative or limited approximation property is presented. Simulation results for sinusoidal function approximation with various quantization levels are shown. The results indicate that the network presents good approximation capability even with severe quantization. The parameter quantization decreases the memory size and circuit complexity required to store the network parameters leading to compact mixed-signal circuits proper for low-power applications. ©2008 IEEE.en
dc.format.extent23-27-
dc.language.isoeng-
dc.sourceScopus-
dc.subjectFunction approximation-
dc.subjectQuantized parameters-
dc.subjectRadial basis function network-
dc.subjectArtificial intelligence-
dc.subjectChlorine compounds-
dc.subjectFeedforward neural networks-
dc.subjectIntelligent control-
dc.subjectNetworks (circuits)-
dc.subjectPolynomial approximation-
dc.subjectApproximation properties-
dc.subjectCircuit complexity-
dc.subjectComputational intelligence-
dc.subjectInternational conferences-
dc.subjectLow-power applications-
dc.subjectMeasurement systems-
dc.subjectMemory size-
dc.subjectMixed-signal circuits-
dc.subjectNetwork parameters-
dc.subjectQuantization levels-
dc.subjectSimulation results-
dc.subjectSinusoidal functions-
dc.subjectRadial basis function networks-
dc.titleRadial basis function networks with quantized parametersen
dc.typeoutro-
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)-
dc.description.affiliationUNESP (Universidade Estadual Paulista), Av. Brasil Norte, 364, Ilha Solteira, SP-
dc.description.affiliationUnespUNESP (Universidade Estadual Paulista), Av. Brasil Norte, 364, Ilha Solteira, SP-
dc.identifier.doi10.1109/CIMSA.2008.4595826-
dc.identifier.wosWOS:000259443400006-
dc.rights.accessRightsAcesso restrito-
dc.relation.ispartofCIMSA 2008 - IEEE Conference on Computational Intelligence for Measurement Systems and Applications Proceedings-
dc.identifier.scopus2-s2.0-52449111383-
Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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