{"id":8534,"date":"1995-01-01T00:00:00","date_gmt":"1995-01-01T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/1995\/01\/01\/algoritmos-para-la-prediccion-de-series-temporales-basados-en-modelos-deterministas-de-baja-dimension\/"},"modified":"1995-01-01T00:00:00","modified_gmt":"1995-01-01T00:00:00","slug":"algoritmos-para-la-prediccion-de-series-temporales-basados-en-modelos-deterministas-de-baja-dimension","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/algoritmos-para-la-prediccion-de-series-temporales-basados-en-modelos-deterministas-de-baja-dimension\/","title":{"rendered":"Algoritmos para la prediccion de series temporales basados en modelos deterministas de baja dimension."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Ramon Huerta Rico <\/strong><\/h2>\n<p>En este trabajo se pone de manifiesto la importancia que tiene para la prediccion de series temporales la construccion de modelos mediante una optima reconstruccion del espacio de estados. Se hace una descripcion detallada de los metodos existentes para reconstruir el espacio de estados explicando sus ventajas y limitaciones. Se propone la estimacion promedio del producto escalar de los vectores de campo como una alternativa robusta y sencilla, que permite obtener la reconstruccion optima cuando otros metodos no muestran indicios de una adecuada reconstruccion.  tambien se propone un algoritmo eficiente para llevar a cabo la estimacion de p.  se analizan diferentes metodos de reconstruccion de modelos globales y se propone un nuevo metodo para la prediccion de series temporales mediante redes neuronales en formato ricatti.  se propone una metodolog\u00eda general que puede ser aplicada a cualquier serie temporal. Esta es utilizada para el control de una planta petroquimica.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Algoritmos para la prediccion de series temporales basados en modelos deterministas de baja dimension.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Algoritmos para la prediccion de series temporales basados en modelos deterministas de baja dimension. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Ramon Huerta Rico <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Aut\u00f3noma de Madrid<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 01\/01\/1995<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Direcci\u00f3n y tribunal<\/h3>\n<ul>\n<li><strong>Director de la tesis<\/strong>\n<ul>\n<li>Vicente Lopez Martinez<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Roberto Moriyon <\/li>\n<li>Jos\u00e9 Mira (vocal)<\/li>\n<li>Manuel Velarde (vocal)<\/li>\n<li>Bert Kappen (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Ramon Huerta Rico En este trabajo se pone de manifiesto la importancia que tiene para la prediccion [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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