{"id":53,"date":"2016-07-12T15:19:40","date_gmt":"2016-07-12T18:19:40","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=53"},"modified":"2016-07-12T15:19:40","modified_gmt":"2016-07-12T18:19:40","slug":"vol1-no1-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol1-no1\/vol1-no1-art1\/","title":{"rendered":"Neuro-fuzzy modelling and control of nonlinear dynamic systems"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> Neuro-fuzzy modelling and control of nonlinear dynamic systems<\/p>\n<p><strong>Autores:<\/strong> Quadrelli, G.; Tanscheit, R.; Vellasco, M. M.<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> The main goal of this paper is to propose procedures for modelling and control of nonlinear systems by using neuro-fuzzy topologies. For the modelling of a nonlinear system, its input space is initially divided into a number of fuzzy operating regions, within which reduced order models represent the system\u2019s behaviour. The complete system modelling \u2013 the global model \u2013 is obtained through the conjunction of the local models by using a neuro-fuzzy network. A neuro-fuzzy adaptive network, based on a hybrid learning algorithm (self-organised learning and supervised learning) and called FALCON-H, is used in the control of a nonlinear plant modelled as described above.<\/p>\n<p><strong>Palavras-chave:<\/strong> Neuro-fuzzy systems; nonlinear systems; modelling; control<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 9<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol1-no1-art1\">10.21528\/lmln-vol1-no1-art1<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol1-no1-art1.pdf\" rel=\"\">vol1-no1-art1.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2016\/07\/vol1-no1-art1.bib\" rel=\"\">vol1-no1-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: Neuro-fuzzy modelling and control of nonlinear dynamic systems Autores: Quadrelli, G.; Tanscheit, R.; Vellasco, M. M. Resumo: The main goal of this paper is to propose procedures for modelling and control of nonlinear systems by using neuro-fuzzy topologies. For <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol1-no1\/vol1-no1-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":30,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-53","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>Neuro-fuzzy modelling and control of nonlinear dynamic systems - 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\/vol1-no1\/vol1-no1-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Neuro-fuzzy modelling and control of nonlinear dynamic systems - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: Neuro-fuzzy modelling and control of nonlinear dynamic systems Autores: Quadrelli, G.; Tanscheit, R.; Vellasco, M. M. Resumo: The main goal of this paper is to propose procedures for modelling and control of nonlinear systems by using neuro-fuzzy topologies. 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