{"id":276,"date":"2016-07-13T16:23:22","date_gmt":"2016-07-13T19:23:22","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=276"},"modified":"2016-07-13T16:23:22","modified_gmt":"2016-07-13T19:23:22","slug":"vol2-no1-art5","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/","title":{"rendered":"Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas<\/p>\n<p><strong>Autores:<\/strong> Luna, Ivette; Ballini, Rosangela; Gomide, Fernando<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> Neste artigo uma classe de estruturas de redes neurais nebulosas recorrentes \u00e9 proposta. Os modelos neurais apresentados s\u00e3o compostos de duas partes: um sistema de infer\u00eancia nebuloso e uma rede neural cl\u00e1ssica. O sistema nebuloso \u00e9 formado por neur\u00f4nios l\u00f3gicos modelados atrav\u00e9s de operadores AND e OR, utilizando normas triangulares. Estes neur\u00f4nios comp\u00f5em a camada intermedi\u00e1ria. A rede neural \u00e9 formada por neur\u00f4nios cl\u00e1ssicos com fun\u00e7\u00f5es de ativa\u00e7\u00e3o n\u00e3o lineares em s\u00e9rie com as unidades l\u00f3gicas pr\u00e9vias. O sistema de infer\u00eancia nebuloso codifica um conjunto de regras do tipo se-ent\u00e3o, sendo a infer\u00eancia efetuada pelos neur\u00f4nios l\u00f3gicos onde ocorre a recorr\u00eancia. Os pesos da camada intermedi\u00e1ria s\u00e3o ajustados utilizando um algoritmo de treinamento por refor\u00e7o associativo e os pesos da sa\u00edda ajustados via o gradiente do erro quadr\u00e1tico. As redes neurais nebulosas recorrentes propostas constituem um meio efetivo para modelagem de sistemas n\u00e3o lineares. Resultados de simula\u00e7\u00e3o envolvendo a identifica\u00e7\u00e3o de um sistema din\u00e2mico n\u00e3o linear mostram que as estruturas propostas fornecem modelos simples, com um mecanismo de aprendizado r\u00e1pido e erros de aproxima\u00e7\u00e3o baixos quando comparados com modelos da literatura.<\/p>\n<p><strong>Palavras-chave:<\/strong> Redes neurais; redes neurais nebulosas; redes recorrentes; modelagem de sistemas<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 11<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol2-no1-art5\">10.21528\/lmln-vol2-no1-art5<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol2-no1-art5.pdf\" rel=\"\">vol2-no1-art5.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol2-no1-art5.bib\" rel=\"\">vol2-no1-art5.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas Autores: Luna, Ivette; Ballini, Rosangela; Gomide, Fernando Resumo: Neste artigo uma classe de estruturas de redes neurais nebulosas recorrentes \u00e9 proposta. Os modelos neurais apresentados s\u00e3o compostos de duas partes: um sistema <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":251,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-276","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas Autores: Luna, Ivette; Ballini, Rosangela; Gomide, Fernando Resumo: Neste artigo uma classe de estruturas de redes neurais nebulosas recorrentes \u00e9 proposta. 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Os modelos neurais apresentados s\u00e3o compostos de duas partes: um sistema Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/","og_site_name":"Learning and NonLinear Models","twitter_card":"summary_large_image","twitter_misc":{"Est. tempo de leitura":"1 minuto"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/","name":"Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"datePublished":"2016-07-13T19:23:22+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/vol2-no1-art5\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 2 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol2-no1\/"},{"@type":"ListItem","position":3,"name":"Incorpora\u00e7\u00e3o de recorr\u00eancia em estruturas neurais nebulosas"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/276","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=276"}],"version-history":[{"count":0,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/276\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/251"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=276"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}