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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampMayer, Kayol Soares-
dc.typeArtigopt_BR
dc.titleBlind fuzzy adaptation step control for a concurrent neural network equalizerpt_BR
dc.contributor.authorMayer, Kayol S.-
dc.contributor.authorDe Oliveira, Matheus S.-
dc.contributor.authorMueller, Candice-
dc.contributor.authorDe Castro, Fernando C. C.-
dc.contributor.authorDe Castro, Maria C. F.-
dc.subjectControle ótimo fuzzypt_BR
dc.subject.otherlanguageFuzzy optimal controlpt_BR
dc.description.abstractMobile communications, not infrequently, are disrupted by multipath propagation in the wireless channel. In this context, this paper proposes a new blind concurrent equalization approach that combines a Phase Transmittance Radial Basis Function Neural Network (PTRBFNN) and the classic Constant Modulus Algorithm (CMA) in a concurrent architecture, with a Fuzzy Controller (FC) responsible for adapting the PTRBFNN and CMA step sizes. Differently from the Neural Network (NN) based equalizers present in literature, the proposed Fuzzy Controller Concurrent Neural Network Equalizer (FC-CNNE) is a completely self-taught concurrent architecture that does not need any training. The Fuzzy Controller inputs are based on the estimated mean squared error of the equalization process and on its variation in time. The proposed solution has been evaluated over standard multipath VHF/UHF channels defined by the International Telecommunication Union. Results show that the FC-CNNE is able to achieve lower residual steady-state MSE value and/or faster convergence rate and consequently lower Bit Error Rate (BER) when compared to Constant Modulus Algorithm-Phase Transmittance Radial Basis Function Neural Network (CMA-PTRBFNN) equalizerpt_BR
dc.relation.ispartofWireless cmmunications and mobile computingpt_BR
dc.relation.ispartofabbreviationWirel. commun. mob. comput.pt_BR
dc.publisher.cityOxfordpt_BR
dc.publisher.countryReino Unidopt_BR
dc.publisherHindawipt_BR
dc.date.issued2019-
dc.date.monthofcirculationJan.pt_BR
dc.language.isoengpt_BR
dc.description.volume2019pt_BR
dc.rightsAbertopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1530-8669pt_BR
dc.identifier.eissn1530-8677pt_BR
dc.identifier.doi10.1155/2019/9082362pt_BR
dc.identifier.urlhttps://www.hindawi.com/journals/wcmc/2019/9082362/pt_BR
dc.description.sponsorshipCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESpt_BR
dc.description.sponsordocumentnumbernão tempt_BR
dc.date.available2020-06-05T15:06:21Z-
dc.date.accessioned2020-06-05T15:06:21Z-
dc.description.provenanceSubmitted by Mariana Aparecida Azevedo (mary1@unicamp.br) on 2020-06-05T15:06:21Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-09-03T11:55:49Z : No. of bitstreams: 1 000456845500001.pdf: 8507094 bytes, checksum: e8ae31ddb4993d39649a8a738bea727a (MD5)en
dc.description.provenanceMade available in DSpace on 2020-06-05T15:06:21Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/342765-
dc.contributor.departmentsem informaçãopt_BR
dc.contributor.unidadeFaculdade de Engenharia Elétrica e de Computaçãopt_BR
dc.subject.keywordMobile communicationspt_BR
dc.subject.keywordNeural Network Equalizerpt_BR
dc.identifier.source000456845500001pt_BR
dc.creator.orcid0000-0002-8837-153Xpt_BR
dc.type.formArtigo de pesquisapt_BR
dc.identifier.articleid9082362pt_BR
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