Multi-stage genetic fuzzy systems based on the iterative rule learning approach

Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contributi...

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Detalles Bibliográficos
Autores: González Muñoz, Antonio, Herrera Triguero, Francisco
Tipo de recurso: artículo
Fecha de publicación:1997
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099/3495
Acceso en línea:https://hdl.handle.net/2099/3495
Access Level:acceso abierto
Palabra clave:Fuzzy logic
Fuzzy rules
Genetic algoritms
Machine learning
GFS
Genetic fuzzy systems
Intel·ligència artificial
Aprenentatge automàtic
Sistemes difusos
Classificació AMS::68 Computer science::68T Artificial intelligence
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oai_identifier_str oai:upcommons.upc.edu:2099/3495
network_acronym_str ES
network_name_str España
repository_id_str
spelling Multi-stage genetic fuzzy systems based on the iterative rule learning approachGonzález Muñoz, AntonioHerrera Triguero, FranciscoFuzzy logicFuzzy rulesGenetic algoritmsMachine learningGFSGenetic fuzzy systemsIntel·ligència artificialAprenentatge automàticSistemes difusosClassificació AMS::68 Computer science::68T Artificial intelligenceGenetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica19971997-01-0120072007-09-17journal articlehttp://purl.org/coar/resource_type/c_6501NAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2099/3495reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099/34952026-05-27T15:37:01Z
dc.title.none.fl_str_mv Multi-stage genetic fuzzy systems based on the iterative rule learning approach
title Multi-stage genetic fuzzy systems based on the iterative rule learning approach
spellingShingle Multi-stage genetic fuzzy systems based on the iterative rule learning approach
González Muñoz, Antonio
Fuzzy logic
Fuzzy rules
Genetic algoritms
Machine learning
GFS
Genetic fuzzy systems
Intel·ligència artificial
Aprenentatge automàtic
Sistemes difusos
Classificació AMS::68 Computer science::68T Artificial intelligence
title_short Multi-stage genetic fuzzy systems based on the iterative rule learning approach
title_full Multi-stage genetic fuzzy systems based on the iterative rule learning approach
title_fullStr Multi-stage genetic fuzzy systems based on the iterative rule learning approach
title_full_unstemmed Multi-stage genetic fuzzy systems based on the iterative rule learning approach
title_sort Multi-stage genetic fuzzy systems based on the iterative rule learning approach
dc.creator.none.fl_str_mv González Muñoz, Antonio
Herrera Triguero, Francisco
author González Muñoz, Antonio
author_facet González Muñoz, Antonio
Herrera Triguero, Francisco
author_role author
author2 Herrera Triguero, Francisco
author2_role author
dc.subject.none.fl_str_mv Fuzzy logic
Fuzzy rules
Genetic algoritms
Machine learning
GFS
Genetic fuzzy systems
Intel·ligència artificial
Aprenentatge automàtic
Sistemes difusos
Classificació AMS::68 Computer science::68T Artificial intelligence
topic Fuzzy logic
Fuzzy rules
Genetic algoritms
Machine learning
GFS
Genetic fuzzy systems
Intel·ligència artificial
Aprenentatge automàtic
Sistemes difusos
Classificació AMS::68 Computer science::68T Artificial intelligence
description Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples.
publishDate 1997
dc.date.none.fl_str_mv 1997
1997-01-01
2007
2007-09-17
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099/3495
url https://hdl.handle.net/2099/3495
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
publisher.none.fl_str_mv Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869422431201918976
score 15,300719

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