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ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IDENTIFICATION OF AN INDUCTION MOTOR

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dc.contributor.author Emam, Ahmed A. M.
dc.contributor.author Tayeb, Eisa Bashier M.
dc.contributor.author Ali, A. Taifour
dc.contributor.author Habiballh, Ammar Hassan
dc.date.accessioned 2016-11-23T10:22:17Z
dc.date.available 2016-11-23T10:22:17Z
dc.date.issued 2013-01-01
dc.identifier.citation Emam, Ahmed A. M.ADAPTIVE NEURO FUZZY INFERENCE SYSTEM IDENTIFICATION OF AN INDUCTION MOTOR/Ahmed A. M. Emam;.-Khartoum:Sudan University of Science and Technology,College of Engineering,2013.-8p en_US
dc.identifier.uri http://repository.sustech.edu/handle/123456789/14703
dc.description Article en_US
dc.description.abstract Modeling and simulation of the squirrel-cage induction motor (SCIM) is very complex and cannot represent the physical system exactly because the system is characterized by highly non-linear, complex and time-varying dynamics and inaccessibility of some of the states and outputs for measurements are depend on a lot of excited input parameters. This work demonstrated experimentally that ANFIS can be effectively used for identification of the system with highly accurate results. The accuracy of the identification results is demonstrated through validation tests including training, testing and validating data. en_US
dc.description.sponsorship Sudan University of Science and Technology en_US
dc.language.iso en en_US
dc.publisher Sudan University of Science and Technology en_US
dc.subject Induction motor en_US
dc.subject Identifiction en_US
dc.subject Neuro-Fuzzy systems en_US
dc.subject ANFIS en_US
dc.subject Hybrid Learning en_US
dc.subject LABVIEW en_US
dc.title ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM IDENTIFICATION OF AN INDUCTION MOTOR en_US
dc.type Article en_US


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