title: Discrete-time reduced order neural observers for uncertain nonlinear systems.
abstract: This paper focusses on a novel discrete-time reduced order neural observer for nonlinear systems, which model is assumed to be unknown. This neural observer is robust in presence of external and internal uncertainties. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm, using a parallel configuration. This work includes the stability proof of the estimation error on the basis of the Lyapunov approach; to illustrate the applicability, simulation results for a nonlinear oscillator are included.
authors.affiliation: CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. Las Aguilas, C.P. 45080, Zapopan, Jalisco, Mexico. almayalanis@gmail.com
authors.forename: Alma Y
authors.initials: AY
authors.lastname: Alanis
authors.forename: Edgar N
authors.initials: EN
authors.lastname: Sanchez
authors.forename: Luis J
authors.initials: LJ
authors.lastname: Ricalde
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted