Weighted Fuzzy Spiking Neural P Systems

Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological be-havior of biological spiking neurons. In order to make SN P sys-tems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P syst...

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Detalles Bibliográficos
Autores: Wang, Jun, Shi, Peng, Peng, Hong, Pérez Jiménez, Mario de Jesús, Wang, Tao
Tipo de recurso: artículo
Estado:Versión enviada para evaluación y publicación
Fecha de publicación:2013
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/79732
Acceso en línea:https://hdl.handle.net/11441/79732
https://doi.org/10.1109/TFUZZ.2012.2208974
Access Level:acceso abierto
Palabra clave:Spiking neural P systems (SN P systems)
Weighted fuzzy production rules
Weighted fuzzy reasoning
Weighted fuzzy spiking neural P systems (WFSN P systems)
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spelling Weighted Fuzzy Spiking Neural P SystemsWang, JunShi, PengPeng, HongPérez Jiménez, Mario de JesúsWang, TaoSpiking neural P systems (SN P systems)Weighted fuzzy production rulesWeighted fuzzy reasoningWeighted fuzzy spiking neural P systems (WFSN P systems)Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological be-havior of biological spiking neurons. In order to make SN P sys-tems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper called weighted fuzzy spiking neural P systems (WFSN P systems). New elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule, and two types of neurons, are added to the original definition of SN P systems. This allows WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm, based on WFSN P systems, is developed, which can ac-complish dynamic fuzzy reasoning of a rule-based system more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph, and Petri nets, to demonstrate the features and advantages of the proposed techniques.IEEE Computer SocietyCiencias de la Computación e Inteligencia ArtificialTIC193: Computación Natural2013info:eu-repo/semantics/articleinfo:eu-repo/semantics/submittedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/79732https://doi.org/10.1109/TFUZZ.2012.2208974reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésIEEE Transactions on Fuzzy Systems, 21 (2), 209-220.https://ieeexplore.ieee.org/document/6242397info:eu-repo/semantics/openAccessoai:idus.us.es:11441/797322026-06-17T12:51:07Z
dc.title.none.fl_str_mv Weighted Fuzzy Spiking Neural P Systems
title Weighted Fuzzy Spiking Neural P Systems
spellingShingle Weighted Fuzzy Spiking Neural P Systems
Wang, Jun
Spiking neural P systems (SN P systems)
Weighted fuzzy production rules
Weighted fuzzy reasoning
Weighted fuzzy spiking neural P systems (WFSN P systems)
title_short Weighted Fuzzy Spiking Neural P Systems
title_full Weighted Fuzzy Spiking Neural P Systems
title_fullStr Weighted Fuzzy Spiking Neural P Systems
title_full_unstemmed Weighted Fuzzy Spiking Neural P Systems
title_sort Weighted Fuzzy Spiking Neural P Systems
dc.creator.none.fl_str_mv Wang, Jun
Shi, Peng
Peng, Hong
Pérez Jiménez, Mario de Jesús
Wang, Tao
author Wang, Jun
author_facet Wang, Jun
Shi, Peng
Peng, Hong
Pérez Jiménez, Mario de Jesús
Wang, Tao
author_role author
author2 Shi, Peng
Peng, Hong
Pérez Jiménez, Mario de Jesús
Wang, Tao
author2_role author
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 Spiking neural P systems (SN P systems)
Weighted fuzzy production rules
Weighted fuzzy reasoning
Weighted fuzzy spiking neural P systems (WFSN P systems)
topic Spiking neural P systems (SN P systems)
Weighted fuzzy production rules
Weighted fuzzy reasoning
Weighted fuzzy spiking neural P systems (WFSN P systems)
description Spiking neural P systems (SN P systems) are a new class of computing models inspired by the neurophysiological be-havior of biological spiking neurons. In order to make SN P sys-tems capable of representing and processing fuzzy and uncertain knowledge, we propose a new class of spiking neural P systems in this paper called weighted fuzzy spiking neural P systems (WFSN P systems). New elements, including fuzzy truth value, certain factor, weighted fuzzy logic, output weight, threshold, new firing rule, and two types of neurons, are added to the original definition of SN P systems. This allows WFSN P systems to adequately characterize the features of weighted fuzzy production rules in a fuzzy rule-based system. Furthermore, a weighted fuzzy backward reasoning algorithm, based on WFSN P systems, is developed, which can ac-complish dynamic fuzzy reasoning of a rule-based system more flexibly and intelligently. In addition, we compare the proposed WFSN P systems with other knowledge representation methods, such as fuzzy production rule, conceptual graph, and Petri nets, to demonstrate the features and advantages of the proposed techniques.
publishDate 2013
dc.date.none.fl_str_mv 2013
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/submittedVersion
format article
status_str submittedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/79732
https://doi.org/10.1109/TFUZZ.2012.2208974
url https://hdl.handle.net/11441/79732
https://doi.org/10.1109/TFUZZ.2012.2208974
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv IEEE Transactions on Fuzzy Systems, 21 (2), 209-220.
https://ieeexplore.ieee.org/document/6242397
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 IEEE Computer Society
publisher.none.fl_str_mv IEEE Computer Society
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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