Análise de técnicas de inteligência artificial para o projeto de enlaces de fibras ópticas

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dc.contributor.advisorOliveira, Rafael Euzébio Pereira de
dc.contributor.advisor-co1Nizam, Omar
dc.contributor.advisor-co1Latteshttp://lattes.cnpq.br/2067336430076971por
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/4273347313516555por
dc.contributor.authorLima, Bruno Cesar dos Santos
dc.creator.Latteshttp://lattes.cnpq.br/7085581279846679por
dc.date.accessioned2020-04-17T00:15:55Z
dc.date.accessioned2020-05-28T18:08:58Z
dc.date.available2020-05-28T18:08:58Z
dc.date.issued2019-12-11
dc.description.abstractSociety currently seeks competitiveness for its business, high performance and a low cost support platform, idealizing the contemporary scenario where the world is interconnected by communication networks. Thus the need for optical networks arises because of its advantages of reaching long distances and high speeds with good bandwidth compared to the wired system, but the optical system itself is limited in its resources favoring the need for research to soften the data. damage caused by distortion of optical signals. The purpose of this paper will use machine learning and artificial intelligence techniques to construct a conceptual model capable of predicting signal distortions in optical link designs and their regeneration and thus ensuring their autonomous optimization, with the aim of reducing the project cost of implementing fiber optic links. The computational potential has increased in the last decades favoring the execution of machine learning algorithms and promoting the conditions for this work. It will be made a comparative analysis between three algorithms already used in the literature in optical communications application seeking to find the most suitable algorithm for the construction of this machine learning model that needs a composite output in its predictions, considering the range of variables necessary to elaborate a optical link. The results presented are motivating, showing a high accuracy of predictions of machine learning algorithms around 99% and in the validation of predictions made an optimized link with a BER 1.10−06 evidencing the application of machine learning algorithms in the projects of optical links.eng
dc.description.sponsorshipFundo Mackenzie de Pesquisapor
dc.formatapplication/pdf*
dc.identifier.citationLIMA, Bruno Cesar dos Santos. Análise de técnicas de inteligência artificial para o projeto de enlaces de fibras ópticas. 2019. 69 f. Dissertação (Mestrado em Engenharia Elétrica e Computação) - Universidade Presbiteriana Mackenzie, São Paulo, 2019.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24506
dc.keywordsartificial intelligenceeng
dc.keywordsmachine learningeng
dc.keywordsartificial neural networkeng
dc.keywordsbayeseng
dc.keywordsoptical communicationseng
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.subjectinteligência artificialpor
dc.subjectmachine learningpor
dc.subjectrede neural artificialpor
dc.subjectbayespor
dc.subjectComunicações ópticaspor
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICApor
dc.titleAnálise de técnicas de inteligência artificial para o projeto de enlaces de fibras ópticaspor
dc.typeDissertaçãopor
local.contributor.board1Lopes, Carlos Magno Baptista
local.contributor.board1Latteshttp://lattes.cnpq.br/3081439765219420por
local.contributor.board2Silva, Leandro Augusto da
local.contributor.board2Latteshttp://lattes.cnpq.br/1396385111251741por
local.publisher.countryBrasilpor
local.publisher.departmentEscola de Engenharia Mackenzie (EE)por
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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