Por favor, use este identificador para citar o enlazar este ítem: https://hdl.handle.net/20.500.12104/40710
Título: Discrete-time neural observers
Autor: Sanchez, E.N.
Alanis, A.Y.
Loukianov, A.G.
Fecha de publicación: 2008
Resumen: This chapter presents the design of an adaptive recurrent neural observer for nonlinear systems, whose mathematical model is assumed to be unknown. The observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme. � 2008 Springer-Verlag Berlin Heidelberg.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-46949104855&partnerID=40&md5=729eeed3be32b26def946c3ab81dfce0
http://hdl.handle.net/20.500.12104/40710
Aparece en las colecciones:Producción científica UdeG

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