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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.
Appears in Collections:Producción científica UdeG

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