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Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems

dc.creatorWang, Taoes
dc.creatorZhang, Gexianges
dc.creatorPérez Jiménez, Mario de Jesúses
dc.date.accessioned2021-12-09T10:43:21Z
dc.date.available2021-12-09T10:43:21Z
dc.date.issued2014
dc.identifier.citationWang, T., Zhang, G. y Pérez Jiménez, M.d.J. (2014). Fault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systems. En CMC 2014: 15th International Conference on Membrane Computing (385-395), Prague, Czech Republic: Springer.
dc.identifier.isbn978-3-319-14369-9es
dc.identifier.issn0302-9743es
dc.identifier.urihttps://hdl.handle.net/11441/128128
dc.description.abstractThis paper discusses the application of fuzzy reasoning spiking neural P systems with real numbers (rFRSN P systems) to fault diagnosis of electric locomotive systems. Relationships among breakdown signals and faulty sections in subsystems of electric locomotive systems are described in the form of fuzzy production rules firstly and then fault diagnosis models based on rFRSN P systems for these subsystems are built according to these rules. Fuzzy production rules for diagnosing electric locomotive systems are abstracted from the fault diagnosis analysis of the subsystems and the causality among faulty sections, faulty subsystems and electric locomotive systems. Finally, a diagnosis model based on rFRSN P systems for electric locomotive systems is proposed.es
dc.description.sponsorshipMinisterio de Economía y Competitividad TIN2012-37434es
dc.formatapplication/pdfes
dc.format.extent11es
dc.language.isoenges
dc.publisherSpringeres
dc.relation.ispartofCMC 2014: 15th International Conference on Membrane Computing (2014), pp. 385-395.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFuzzy reasoning spiking neural P systemes
dc.subjectFault diagnosises
dc.subjectElectric locomotive systemes
dc.subjectReal numberes
dc.subjectSS4 electric locomotive systemses
dc.titleFault Diagnosis Models for Electric Locomotive Systems Based on Fuzzy Reasoning Spiking Neural P Systemses
dc.typeinfo:eu-repo/semantics/conferenceObjectes
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.relation.projectIDTIN2012-37434es
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-14370-5_24es
dc.identifier.doi10.1007/978-3-319-14370-5_24es
dc.contributor.groupUniversidad de Sevilla. TIC193 : Computación Naturales
dc.publication.initialPage385es
dc.publication.endPage395es
dc.eventtitleCMC 2014: 15th International Conference on Membrane Computinges
dc.eventinstitutionPrague, Czech Republices
dc.relation.publicationplaceCham, Switzerlandes
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). Españaes

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