Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis

This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference abil...

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Detalles Bibliográficos
Autores: Wang, Tao, Zhang, Gexiang, Rong, Haina, Pérez Jiménez, Mario de Jesús
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2014
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/108010
Acceso en línea:https://hdl.handle.net/11441/108010
https://doi.org/10.15837/ijccc.2014.6.1485
Access Level:acceso abierto
Palabra clave:fuzzy reasoning spiking neural P system with trapezoidal fuzzy number
Fuzzy reasoning
Fault diagnosis
trapezoidal fuzzy number
linguistic term
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spelling Application of Fuzzy Reasoning Spiking Neural P Systems to Fault DiagnosisWang, TaoZhang, GexiangRong, HainaPérez Jiménez, Mario de Jesúsfuzzy reasoning spiking neural P system with trapezoidal fuzzy numberFuzzy reasoningFault diagnosistrapezoidal fuzzy numberlinguistic termThis paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process.Agora University of Oradea, RomaniaCiencias de la Computación e Inteligencia ArtificialTIC193: Computación Natural2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/108010https://doi.org/10.15837/ijccc.2014.6.1485reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésInternational Journal of Computers, Communications and Control, 9 (6), 786-799.http://univagora.ro/jour/index.php/ijccc/article/view/1485info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1080102026-06-17T12:51:07Z
dc.title.none.fl_str_mv Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
title Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
spellingShingle Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
Wang, Tao
fuzzy reasoning spiking neural P system with trapezoidal fuzzy number
Fuzzy reasoning
Fault diagnosis
trapezoidal fuzzy number
linguistic term
title_short Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
title_full Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
title_fullStr Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
title_full_unstemmed Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
title_sort Application of Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis
dc.creator.none.fl_str_mv Wang, Tao
Zhang, Gexiang
Rong, Haina
Pérez Jiménez, Mario de Jesús
author Wang, Tao
author_facet Wang, Tao
Zhang, Gexiang
Rong, Haina
Pérez Jiménez, Mario de Jesús
author_role author
author2 Zhang, Gexiang
Rong, Haina
Pérez Jiménez, Mario de Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Ciencias de la Computación e Inteligencia Artificial
TIC193: Computación Natural
dc.subject.none.fl_str_mv fuzzy reasoning spiking neural P system with trapezoidal fuzzy number
Fuzzy reasoning
Fault diagnosis
trapezoidal fuzzy number
linguistic term
topic fuzzy reasoning spiking neural P system with trapezoidal fuzzy number
Fuzzy reasoning
Fault diagnosis
trapezoidal fuzzy number
linguistic term
description This paper discusses the application of fuzzy reasoning spiking neural P systems with trapezoidal fuzzy numbers (tFRSN P systems) to fault diagnosis of power systems, where a matrix-based fuzzy reasoning algorithm based on the dynamic firing mechanism of neurons is used to develop the inference ability of tFRSN P systems from classical reasoning to fuzzy reasoning. Some case studies show the effectiveness of the presented method. We also briefly draw comparisons between the presented method and several main fault diagnosis approaches from the perspectives of knowledge representation and inference process.
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/108010
https://doi.org/10.15837/ijccc.2014.6.1485
url https://hdl.handle.net/11441/108010
https://doi.org/10.15837/ijccc.2014.6.1485
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv International Journal of Computers, Communications and Control, 9 (6), 786-799.
http://univagora.ro/jour/index.php/ijccc/article/view/1485
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Agora University of Oradea, Romania
publisher.none.fl_str_mv Agora University of Oradea, Romania
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.300719

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