Abstract
This paper deals with the problem of discrete-time nonlinear system identification via Recurrent High Order Neural Networks. It includes the respective stability analysis on the basis of the Lyapunov approach for the extended Kalman filter (EKF)-based NN training algorithm, which is applied for learning. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.
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Alanis, A.Y., Sanchez, E.N., Loukianov, A.G., Perez-Cisneros, M.A. (2010). Discrete Time Nonlinear Identification via Recurrent High Order Neural Networks for a Three Phase Induction Motor. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_91
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DOI: https://doi.org/10.1007/978-3-642-13278-0_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13277-3
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