{"id":5715,"date":"1995-01-01T00:00:00","date_gmt":"1995-01-01T00:00:00","guid":{"rendered":"https:\/\/www.deberes.net\/tesis\/1995\/01\/01\/estudio-aplicaciones-y-optimizacion-de-algoritmos-neuronales-de-cuantizacion-vectorial-mediante-algoritmos-geneticos\/"},"modified":"1995-01-01T00:00:00","modified_gmt":"1995-01-01T00:00:00","slug":"estudio-aplicaciones-y-optimizacion-de-algoritmos-neuronales-de-cuantizacion-vectorial-mediante-algoritmos-geneticos","status":"publish","type":"post","link":"https:\/\/www.deberes.net\/tesis\/matematicas\/estudio-aplicaciones-y-optimizacion-de-algoritmos-neuronales-de-cuantizacion-vectorial-mediante-algoritmos-geneticos\/","title":{"rendered":"Estudio, aplicaciones y optimizacion de algoritmos neuronales de cuantizacion vectorial mediante algoritmos geneticos."},"content":{"rendered":"<h2>Tesis doctoral de <strong> Juan  Julian Merelo Guervos <\/strong><\/h2>\n<p>En esta tesis se presenta un nuevo algoritmo de clasificacion denominado g-lvq. Este algoritmo consiste basicamente en una optimizacion con pocos parametros libres del algoritmo de clasificacion supervisada lvq de kohonen.  la optimizacion de la red neuronal lvq se lleva a cabo utilizando algoritmos geneticos, que son potentes metodos de optimizacion basados en la seleccion natural y la base molecular de la misma.  para optimizar una red lvq, se codifica cada red en un \u00abcromosoma\u00bb y se crea una poblacion de los mismos. Cada red es evaluada en una tarea de clasificacion, y dependiendo de su exito en esta tarea, se le asigna una puntuacion que consiste en la exactitud en la clasificacion, el tama\u00f1o final de la red, y la distorsion o error entre el conjunto de entrada y la red neuronal obtenida. Los \u00abcromosomas\u00bb correspondientes a las redes neuronales con mas exito se entrecruzaran y mutaran, dando lugar a nuevas redes que seran tambien evaluadas.  ademas, se introducen nuevos operadores geneticos, que permiten alterar la longitud de los cromosomas. Estos operadores aumentan la longitud de la red neuronal siempre que alguna neurona gane demasiadas veces para muestras de entrada, y se disminuye la longitud siempre que alguna neurona no gane nunca. El algoritmo g-lvq esta preparado para ejecutarse en arquitecturas de tipo hipercubo o rejilla de procesadores, ya que todas las operaciones sobre genomas y redes neuronales se realizan a nivel local. Los resultados obtenidos en tareas de clasificacion mejoran sustancialmente a los obtenidos con otros algoritmos clasicos.<\/p>\n<p>&nbsp;<\/p>\n<h3>Datos acad\u00e9micos de la tesis doctoral \u00ab<strong>Estudio, aplicaciones y optimizacion de algoritmos neuronales de cuantizacion vectorial mediante algoritmos geneticos.<\/strong>\u00ab<\/h3>\n<ul>\n<li><strong>T\u00edtulo de la tesis:<\/strong>\u00a0 Estudio, aplicaciones y optimizacion de algoritmos neuronales de cuantizacion vectorial mediante algoritmos geneticos. <\/li>\n<li><strong>Autor:<\/strong>\u00a0 Juan  Julian Merelo Guervos <\/li>\n<li><strong>Universidad:<\/strong>\u00a0 Granada<\/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>Alberto Prieto Espinosa<\/li>\n<\/ul>\n<\/li>\n<li><strong>Tribunal<\/strong>\n<ul>\n<li>Presidente del tribunal: Francisco Sandoval Hernandez <\/li>\n<li> Siguenza Pizarro Juan  Alberto (vocal)<\/li>\n<li>Federico Mor\u00e1n Abad (vocal)<\/li>\n<li>M. Reyneri Leonardo (vocal)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Tesis doctoral de Juan Julian Merelo Guervos En esta tesis se presenta un nuevo algoritmo de clasificacion denominado g-lvq. Este [&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 center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-gradient":""}},"footnotes":""},"categories":[1890,13880,2528,126],"tags":[21132,4797,4800,22180,22181,4799],"class_list":["post-5715","post","type-post","status-publish","format-standard","hentry","category-ciencia-de-los-ordenadores","category-informatica","category-inteligencia-artificial","category-matematicas","tag-alberto-prieto-espinosa","tag-federico-moran-abad","tag-francisco-sandoval-hernandez","tag-juan-julian-merelo-guervos","tag-m-reyneri-leonardo","tag-siguenza-pizarro-juan-alberto"],"_links":{"self":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/5715","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/comments?post=5715"}],"version-history":[{"count":0,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/posts\/5715\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/media?parent=5715"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/categories?post=5715"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.deberes.net\/tesis\/wp-json\/wp\/v2\/tags?post=5715"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}