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dc.contributor.authorTu, M.-
dc.contributor.authorWang, J.-
dc.contributor.authorPeng, H.-
dc.contributor.authorShi, P.-
dc.identifier.citationChinese Journal of Electronics, 2014; 23(1):87-92-
dc.description.abstractAdaptive fuzzy spiking neural P systems (AFSN P systems) are a novel kind of computing models with parallel computing and learning ability. Based on our existing works, AFSN P systems are applied to deal with the fault diagnosis problems of power systems and the uncertainty of action messages about protective relays and breakers, and a new fault diagnosis model of power systems is proposed with simple reasoning process and fast speed with parallel processing capabilities. The effectiveness of the fault diagnosis model is verified by some examples of fault diagnosis. Furthermore, the learning ability of AFSN P systems can be applied to adjust the weights in the fault diagnosis model automatically.-
dc.description.statementofresponsibilityTu Min, Wang Jun, Peng Hong, Shi Peng-
dc.publisherTechnology Exchange Limited Hong Kong-
dc.rightsCopyright status unknown-
dc.subjectMembrane computing; spiking neural P systems; fault diagnosis; power systems-
dc.titleApplication of adaptive fuzzy spiking neural P systems in fault diagnosis of power systems-
dc.typeJournal article-
dc.identifier.orcidShi, P. [0000-0001-8218-586X]-
Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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