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dc.creatorWang, Tao
dc.creatorZhang, Gexiang
dc.creatorPérez Jiménez, Mario de Jesús
dc.description.abstractThis paper discusses the application of weighted fuzzy reasoning spiking neu- ral P systems (WFRSN P systems) to fault diagnosis in traction power supply systems (TPSSs) of China high-speed railways. Four types of neurons are considered in WFRSN P systems to make them suitable for expressing status information of protective relays and circuit breakers, and a weighted matrix-based reasoning algorithm (WMBRA) is intro- duced to fulfill the reasoning based on the status information to obtain fault confidence levels of faulty sections. Fault diagnosis production rules in TPSSs and their WFRSN P system models are proposed to show how to use WFRSN P systems to describe different kinds of fault information. Building processes of fault diagnosis models for sections and fault region identification of feeding sections, and parameter setting of the models are described in detail. Case studies including normal power supply and over zone feeding show the effectiveness of the presented
dc.description.sponsorshipMinisterio de Economía y Competitividad TIN 2012-3734
dc.publisherFénix Editoraes
dc.relation.ispartofProceedings of the Twelfth Brainstorming Week on Membrane Computing, 329-350. Sevilla, E.T.S. de Ingeniería Informática, 3-7 de Febrero, 2014,es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.titleApplication of Weighted Fuzzy Reasoning Spiking Neural P Systems to Fault Diagnosis in Traction Power Supply Systems of High-speed Railwayses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Ciencias de la Computación e Inteligencia Artificiales
dc.contributor.groupUniversidad de Sevilla. TIC193: Computación Natural
dc.contributor.funderMinisterio de Economía y Competitividad (MINECO). España

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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional