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dc.contributor.authorMarar, João Fernando [UNESP]
dc.contributor.authorPatrocinio, Ana Claudia [UNESP]
dc.date.accessioned2014-05-27T11:19:41Z
dc.date.available2014-05-27T11:19:41Z
dc.date.issued1999-01-01
dc.identifierhttp://dx.doi.org/10.1117/12.357191
dc.identifier.citationSignal Processing, Sensor Fusion, and Target Recognition Viii. Bellingham: Spie-int Soc Optical Engineering, v. 3720, p. 451-458, 1999.
dc.identifier.issn0277-786X
dc.identifier.urihttp://hdl.handle.net/11449/130718
dc.description.abstractFunction approximation is a very important task in environments where the computation has to be based on extracting information from data samples in real world processes. So, the development of new mathematical model is a very important activity to guarantee the evolution of the function approximation area. In this sense, we will present the Polynomials Powers of Sigmoid (PPS) as a linear neural network. In this paper, we will introduce one series of practical results for the Polynomials Powers of Sigmoid, where we will show some advantages of the use of the powers of sigmiod functions in relationship the traditional MLP-Backpropagation and Polynomials in functions approximation problems.en
dc.format.extent451-458
dc.language.isoeng
dc.publisherSpie - Int Soc Optical Engineering
dc.relation.ispartofProceedings of SPIE - The International Society for Optical Engineering
dc.sourceScopus
dc.subjectApproximation theory
dc.subjectBackpropagation
dc.subjectFunction evaluation
dc.subjectPolynomials
dc.subjectFunction approximation
dc.subjectPolynomials powers of sigmoid (PPS)
dc.subjectMultilayer neural networks
dc.titleComparative study between powers of sigmoid functions, MLP-backpropagation and polynomials in function approximation problemsen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://proceedings.spiedigitallibrary.org/ss/TermsOfUse.aspx
dcterms.rightsHolderSpie-int Soc Optical Engineering
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.description.affiliationUniv Estadual Paulista, UNESP, Adapt Syst & Intelligent Comp Lab, Dept Comp Sci, Bauru, SP, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, UNESP, Adapt Syst & Intelligent Comp Lab, Dept Comp Sci, Bauru, SP, Brazil
dc.identifier.doi10.1117/12.357191
dc.identifier.wosWOS:000082902100045
dc.rights.accessRightsAcesso aberto
dc.identifier.scopus2-s2.0-0032683364
unesp.campusUniversidade Estadual Paulista (Unesp), Faculdade de Ciências, Baurupt
dc.identifier.lattes1233049484488761
unesp.author.lattes1233049484488761
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