A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model

Autores
Wen, Jiwen; Li, Daoliang; Zhu, Wei; Fu, Zetian
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge’s representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases
IFIP International Conference on Artificial Intelligence in Theory and Practice - Expert Systems
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
fish-disease diagnosis
parsimonious covering theory
fuzzy set theory
Expert system tools and techniques
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:10915/23966
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/23966
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference modelWen, JiwenLi, DaoliangZhu, WeiFu, ZetianCiencias Informáticasfish-disease diagnosisparsimonious covering theoryfuzzy set theoryExpert system tools and techniquesThere are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge’s representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic casesIFIP International Conference on Artificial Intelligence in Theory and Practice - Expert SystemsRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23966enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info: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:UNLP2025-09-29T10:55:40Zoai:sedici.unlp.edu.ar:10915/23966Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:40.653SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
title A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
spellingShingle A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
Wen, Jiwen
Ciencias Informáticas
fish-disease diagnosis
parsimonious covering theory
fuzzy set theory
Expert system tools and techniques
title_short A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
title_full A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
title_fullStr A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
title_full_unstemmed A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
title_sort A new method for fish-disease diagnostic problem solving based on parsimonious covering theory and fuzzy inference model
dc.creator.none.fl_str_mv Wen, Jiwen
Li, Daoliang
Zhu, Wei
Fu, Zetian
author Wen, Jiwen
author_facet Wen, Jiwen
Li, Daoliang
Zhu, Wei
Fu, Zetian
author_role author
author2 Li, Daoliang
Zhu, Wei
Fu, Zetian
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
fish-disease diagnosis
parsimonious covering theory
fuzzy set theory
Expert system tools and techniques
topic Ciencias Informáticas
fish-disease diagnosis
parsimonious covering theory
fuzzy set theory
Expert system tools and techniques
dc.description.none.fl_txt_mv There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge’s representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases
IFIP International Conference on Artificial Intelligence in Theory and Practice - Expert Systems
Red de Universidades con Carreras en Informática (RedUNCI)
description There are three kinds of uncertainty in the process of fish-disease diagnosis, such as randomicity, fuzzy and imperfection, which affect the veracity of fish-disease diagnostic conclusion. So, it is important to construct a fish-disease diagnostic model to effectively deal with these uncertainty knowledge’s representation and reasoning. In this paper, the well-developed parsimonious covering theory capable of handling randomicity knowledge is extended. A fuzzy inference model capable of handling fuzzy knowledge is proposed, and the corresponding algorithms based the sequence of obtaining manifestations are provided to express imperfection knowledge. In the last, the model is proved to be effective and practicality through a set of fish-disease diagnostic cases
publishDate 2006
dc.date.none.fl_str_mv 2006-08
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/10915/23966
url http://sedici.unlp.edu.ar/handle/10915/23966
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6
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
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
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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