Prior knowledge in evolutionary fuzzy recurrent controllers design

Autores
Apolloni, Javier; Kavka, Carlos; Roggero, Patricia
Año de publicación
2005
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Prior Knowledge
Evolutionary Fuzzy Recurrent
Controllers Design
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/21160
Acceder
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network_name_str SEDICI (UNLP)
spelling Prior knowledge in evolutionary fuzzy recurrent controllers designApolloni, JavierKavka, CarlosRoggero, PatriciaCiencias InformáticasARTIFICIAL INTELLIGENCEPrior KnowledgeEvolutionary Fuzzy RecurrentControllers DesignA fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.Eje: Inteligencia artificialRed de Universidades con Carreras en Informática (RedUNCI)2005-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf349-356http://sedici.unlp.edu.ar/handle/10915/21160enginfo:eu-repo/semantics/altIdentifier/isbn/950-665-337-2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:27:21Zoai:sedici.unlp.edu.ar:10915/21160Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:21.778SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Prior knowledge in evolutionary fuzzy recurrent controllers design
title Prior knowledge in evolutionary fuzzy recurrent controllers design
spellingShingle Prior knowledge in evolutionary fuzzy recurrent controllers design
Apolloni, Javier
Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Prior Knowledge
Evolutionary Fuzzy Recurrent
Controllers Design
title_short Prior knowledge in evolutionary fuzzy recurrent controllers design
title_full Prior knowledge in evolutionary fuzzy recurrent controllers design
title_fullStr Prior knowledge in evolutionary fuzzy recurrent controllers design
title_full_unstemmed Prior knowledge in evolutionary fuzzy recurrent controllers design
title_sort Prior knowledge in evolutionary fuzzy recurrent controllers design
dc.creator.none.fl_str_mv Apolloni, Javier
Kavka, Carlos
Roggero, Patricia
author Apolloni, Javier
author_facet Apolloni, Javier
Kavka, Carlos
Roggero, Patricia
author_role author
author2 Kavka, Carlos
Roggero, Patricia
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Prior Knowledge
Evolutionary Fuzzy Recurrent
Controllers Design
topic Ciencias Informáticas
ARTIFICIAL INTELLIGENCE
Prior Knowledge
Evolutionary Fuzzy Recurrent
Controllers Design
dc.description.none.fl_txt_mv A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.
Eje: Inteligencia artificial
Red de Universidades con Carreras en Informática (RedUNCI)
description A fuzzy controller is usually designed by formulating the knowledge of a human expert into a set of linguistic variables and fuzzy rules. As it is well known, the use of prior knowledge can dramatically improve the performance and quality of the fuzzy system design process. In previous works we have introduced the RFV model, a representation for recurrent fuzzy controllers based on Voronoi diagrams that can represent fuzzy systems with synergistic rules, ful lling the completeness property and providing a simple way to introduce prior knowledge. In this work we present our current approach in the study of the inclusion of prior knowledge in the context of the RFV model.
publishDate 2005
dc.date.none.fl_str_mv 2005-05
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/21160
url http://sedici.unlp.edu.ar/handle/10915/21160
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/950-665-337-2
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
349-356
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 13.13397

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