{"id":24671,"date":"2003-11-07T00:00:00","date_gmt":"2003-11-07T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/sin-categoria\/tecnicas-de-agrupamiento-para-identificacion-y-control-por-modelos-locales\/"},"modified":"2003-11-07T00:00:00","modified_gmt":"2003-11-07T00:00:00","slug":"tecnicas-de-agrupamiento-para-identificacion-y-control-por-modelos-locales","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/politecnica-de-valencia\/tecnicas-de-agrupamiento-para-identificacion-y-control-por-modelos-locales\/","title":{"rendered":"T\u00e9cnicas de agrupamiento para identificaci\u00f3n y control por modelos locales"},"content":{"rendered":"<h2>Tesis doctoral de <strong>  D\u00edez Ruano Jos\u00e9 Luis <\/strong><\/h2>\n<p>Una opci\u00f3n interesante para el modelado de sistemas no lineales son los modelos borrosos, sin olvidar que una de las metas del modelado borroso es la interpretabilidad de los modelos. Adem\u00e1s, si se pretenden usar los modelos identificados para una posterior aplicaci\u00f3n a control, ser\u00e1 necesario tener en cuenta algunas consideraciones adicionales que podr\u00edan modificar los criterios empleados en identificaci\u00f3n para predicci\u00f3n. En esta tesis, tras resaltar algunos conceptos poco claros relacionados con los criterios de modelado local entronados en la bibliograf\u00eda, se proponen las caracter\u00edsticas deseables de las t\u00e9cnicas de modelado e identificaci\u00f3n borrosa cuando el prop\u00f3sito es el control por modelos locales.  tras estudiar el comportamiento de las funciones objetivo de los algoritmos de agrupamiento borroso adecuados en el modelado para control, se presenta un nuevo \u00edndice que incorpora algunas caracter\u00edsticas deseables relacionadas con la convexidad y suavidad de las clases finales identificadas, con ventajas respecto a otras metodolog\u00edas como gustaffson-kessel.  a continuaci\u00f3n se define una familia de algoritmos de agrupamiento que minimizan la funci\u00f3n objetivo previamente definida. Estos algoritmos lelvan a una identificaci\u00f3n m\u00e1s adecuada de modelos locales, aproximando los modelos locales el modelo linealizado de la planta en sus zonas de validez y, adicionalmente, con caracter\u00edsticas deseables respecto a la interpretabilidad de las funciones de pertencnia (convexidad y suavidad).  el algoritmo proporciona simult\u00e1neamente modelos locales lineales y agrupamiento de los datos de entrada, siendo especialmente aplicable para identificaci\u00f3n de modelos borrosos takagi-sugeno y descomposici\u00f3n de sistemas no lineales en modelos locales lineales para control.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>T\u00e9cnicas de agrupamiento para identificaci\u00f3n y control por modelos locales<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 T\u00e9cnicas de agrupamiento para identificaci\u00f3n y control por modelos locales <\/li>\n<li><strong>Autor:<\/strong>\u00a0  D\u00edez Ruano Jos\u00e9 Luis <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Polit\u00e9cnica de Valencia<\/li>\n<li><strong>Fecha de lectura de la tesis:<\/strong>\u00a0 11\/07\/2003<\/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> Navarro Herrero Jos\u00e9 Luis<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: pedro Alberto p\u00e9rez <\/li>\n<li>ram\u00f3n Gal\u00e1n l\u00f3pez (vocal)<\/li>\n<li>Luis Bas\u00e1\u00f1ez villaluenga (vocal)<\/li>\n<li>vicent Botti navarro (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de D\u00edez Ruano Jos\u00e9 Luis Una opci\u00f3n interesante para el modelado de sistemas no lineales son los modelos [&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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