Real-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks

dc.creatorAlanis, Alma Y.
dc.creatorSánchez, Edgar N.
dc.creatorLoukianov, Alexander G.
dc.creatorPérez, Marco A.
dc.date.accessioned2013-04-25T17:18:36Z
dc.date.available2013-04-25T17:18:36Z
dc.date.created2013-04-25T17:18:36Z
dc.date.issued2010-09-30
dc.identifierRevista Computación y Sistemas; Vol. 14 No.1
dc.identifier1405-5546
dc.identifierhttp://www.repositoriodigital.ipn.mx/handle/123456789/15420
dc.description.abstractAbstract. This paper deals with the discrete-time nonlinear system identification via Recurrent High Order Neural Networks, trained with an extended Kalman filter (EKF) based algorithm. The paper also includes the respective stability analysis on the basis of the Lyapunov approach for the whole scheme. Applicability of the scheme is illustrated via real-time implementation for a three phase induction motor.
dc.languageen_US
dc.publisherRevista Computación y Sistemas; Vol. 14 No.1
dc.relationRevista Computación y Sistemas;Vol. 14 No.1
dc.subjectKeywords. Neural identification, Extended Kalman filtering learning, Discrete-time nonlinear systems, Three phase induction motor.
dc.titleReal-time Discrete Nonlinear Identification via Recurrent High Order Neural Networks
dc.typeArticle


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