{"id":1090,"date":"2019-03-08T11:34:57","date_gmt":"2019-03-08T14:34:57","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1090"},"modified":"2019-03-08T11:34:57","modified_gmt":"2019-03-08T14:34:57","slug":"vol16-no1-art1","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/","title":{"rendered":"CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA"},"content":{"rendered":"<p><strong>T\u00edtulo:<\/strong> CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA<\/p>\n<p><strong>Autores:<\/strong> Eisencraft, Marcio<\/p>\n<p align=\"justify\"><strong>Resumo:<\/strong> No presente texto revisitam-se alguns aspectos de sinais ca\u00f3ticos gerados por sistemas de tempo discreto ou mapas. Em particular, destaca-se o emprego de t\u00e9cnicas comumente utilizadas para sinais aleat\u00f3rios na sua caracteriza\u00e7\u00e3o. Para tanto, inicia-se com uma breve revis\u00e3o das principais defini\u00e7\u00f5es e propriedades dos sinais ca\u00f3ticos de tempo discreto, exemplificando-se tamb\u00e9m diversos mapas capazes de ger\u00e1-los. A seguir, as t\u00e9cnicas e conceitos apresentados s\u00e3o utilizados na dedu\u00e7\u00e3o das caracter\u00edsticas temporais e espectrais de sinais gerados por alguns mapas lineares por partes. Por fim, sugerem-se temas de pesquisa futuros.<\/p>\n<p><strong>Palavras-chave:<\/strong> Sistemas din\u00e2micos, Processamento de sinais, Sistemas n\u00e3o lineares, Teoria do caos, Comunica\u00e7\u00f5es.<\/p>\n<p><strong>P\u00e1ginas:<\/strong> 25<\/p>\n<p><strong>C\u00f3digo DOI:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/LNLM-vol16-no1-art1\">10.21528\/LNLM-vol16-no1-art1<\/a><\/p>\n<p><strong>Artigo em PDF:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol16-no1-art1.pdf\" rel=\"\">vol16-no1-art1.pdf<\/a><\/p>\n<p><strong>Arquivo BibTex:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2019\/03\/vol16-no1-art1.bib\" rel=\"\">vol16-no1-art1.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>T\u00edtulo: CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA Autores: Eisencraft, Marcio Resumo: No presente texto revisitam-se alguns aspectos de sinais ca\u00f3ticos gerados por sistemas de tempo discreto ou mapas. Em particular, destaca-se o emprego de t\u00e9cnicas comumente utilizadas <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1059,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1090","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>CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA - 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\/vol16-no1\/vol16-no1-art1\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"T\u00edtulo: CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA Autores: Eisencraft, Marcio Resumo: No presente texto revisitam-se alguns aspectos de sinais ca\u00f3ticos gerados por sistemas de tempo discreto ou mapas. 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Em particular, destaca-se o emprego de t\u00e9cnicas comumente utilizadas Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/","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\/vol16-no1\/vol16-no1-art1\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/","name":"CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"datePublished":"2019-03-08T14:34:57+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/vol16-no1-art1\/#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 16 &#8211; N\u00famero 1","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol16-no1\/"},{"@type":"ListItem","position":3,"name":"CAOS EM TEMPO DISCRETO: INTRODU\u00c7\u00c3O, EXEMPLOS E REPRESENTA\u00c7\u00c3O ESTAT\u00cdSTICA"}]},{"@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\/1090","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=1090"}],"version-history":[{"count":0,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1090\/revisions"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1059"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=1090"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}