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Paraconsistent Artificial Neural Networks and EEG Analysis

dc.contributor.authorAbe, Jair Minoro [UNESP]
dc.contributor.authorLopes, Helder F. S.
dc.contributor.authorNakamatsu, Kazumi
dc.contributor.authorAkama, Seiki
dc.contributor.authorSetchi, R.
dc.contributor.authorJordanov, I
dc.contributor.authorHowlett, R. J.
dc.contributor.authorJain, L. C.
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniv Hyogo
dc.date.accessioned2020-12-10T16:35:41Z
dc.date.available2020-12-10T16:35:41Z
dc.date.issued2010-01-01
dc.description.abstractThe aim of this paper is to present a study of brain EEG waves through a new ANN based on Paraconsistent Annotated Evidential Logic E tau which is capable of manipulating concepts like impreciseness, inconsistency, and paracompleteness in a nontrivial manner. As application, the Paraconsistent Artificial Neural Network - PANN showed capable of recognizing children with Dyslexia with Kappa index at a rate of 80%.en
dc.description.affiliationUniv Estadual Paulista, ICET, Grad Program Prod Engn, R Dr Bacelar 1212, BR-04026002 Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Inst Adv Studies, BR-05508 Sao Paulo, Brazil
dc.description.affiliationUniv Hyogo, Sch Human Sci & Environm, Kobe, Hyogo 6500044, Japan
dc.description.affiliationUnespUniv Estadual Paulista, ICET, Grad Program Prod Engn, R Dr Bacelar 1212, BR-04026002 Sao Paulo, Brazil
dc.format.extent164-+
dc.identifier.citationKnowledge-based And Intelligent Information And Engineering Systems, Pt Iii. Berlin: Springer-verlag Berlin, v. 6278, p. 164-+, 2010.
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11449/194726
dc.identifier.wosWOS:000289402900019
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofKnowledge-based And Intelligent Information And Engineering Systems, Pt Iii
dc.sourceWeb of Science
dc.subjectartificial neural network
dc.subjectparaconsistent logics
dc.subjectannotated logics
dc.subjectpattern recognition
dc.subjectDyslexia
dc.titleParaconsistent Artificial Neural Networks and EEG Analysisen
dc.typeTrabalho apresentado em evento
dcterms.licensehttp://www.springer.com/open+access/authors+rights?SGWID=0-176704-12-683201-0
dcterms.rightsHolderSpringer
unesp.author.orcid0000-0003-2088-9065[1]

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