Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12104/44131
Title: Reduced neural observers for a class of MIMO discrete-time nonlinear system
Author: Alanis, A.Y.
Sanchez, E.N.
Hernandez, E.A.
Issue Date: 2009
Abstract: A nonlinear discrete-time reduced order neural observer for the state estimation of a discrete-time unknown nonlinear system, in presence of external and internal uncertainties is presented. The observer is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. This observer estimates the state of the unknown discrete-time nonlinear system, using a parallel configuration. The paper also includes the stability proof on the basis of the Lyapunov approach. To illustrate the applicability simulation results are included.
URI: http://www.scopus.com/inward/record.url?eid=2-s2.0-77949805108&partnerID=40&md5=36a7d7e3f6cb58bce75f0eccbdb4bd46
http://hdl.handle.net/20.500.12104/44131
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