{"id":296,"date":"2016-07-13T17:21:26","date_gmt":"2016-07-13T20:21:26","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=296"},"modified":"2016-07-13T17:21:26","modified_gmt":"2016-07-13T20:21:26","slug":"vol3-no1-art2","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol3-no1\/vol3-no1-art2\/","title":{"rendered":"\u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> \u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas<\/p>\n<p><strong>Autores:<\/strong> Ballini, Rosangela; Gomide, Fernando<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> A novel learning algorithm for recurrent fuzzy neural network is introduced in this paper. The core of the learning algorithm uses equality index as the performance measure to be optimized. Equality index is especially important because its properties reflect the fuzzy set-based structure of the neural network and nature of learning. Equality indexes are strongly tied with the properties of the fuzzy set theory and logic-based techniques. The neural network recurrent topology is built with fuzzy neuron units and performs neural processing consistent with fuzzy system methodology. Therefore neural processing and learning are fully embodied within fuzzy set theory. The performance recurrent fuzzy neural network is verified via examples of learning sequences. Computational experiments show that the recurrent fuzzy neural models developed are simpler and that learning is faster than both, static neural and time series models.<\/p>\n<p><strong>Palavras-chave:<\/strong> Redes neurais; redes neurofuzzy recorrentes; previs\u00e3o de s\u00e9ries temporais<\/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-vol3-no1-art2\">10.21528\/lmln-vol3-no1-art2<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol3-no1-art2.pdf\" rel=\"\">vol3-no1-art2.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol3-no1-art2.bib\" rel=\"\">vol3-no1-art2.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: \u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas Autores: Ballini, Rosangela; Gomide, Fernando Resumo: A novel learning algorithm for recurrent fuzzy neural network is introduced in this paper. The core of the learning <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol3-no1\/vol3-no1-art2\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":290,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-296","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>\u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas - 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\/vol3-no1\/vol3-no1-art2\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"\u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: \u00cdndice de Igualdade e Aprendizado em Redes Neurofuzzy Recorrentes em Previs\u00e3o de S\u00e9ries Macroecon\u00f4micas Autores: Ballini, Rosangela; Gomide, Fernando Resumo: A novel learning algorithm for recurrent fuzzy neural network is introduced in this paper. 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