CUCEI, Universidad de Guadalajara, Apartado Postal 51-71, Col. Las Aguilas, C.P. 45080, Zapopan, Jalisco, Mexico.
Int J Neural Syst. 2010 Feb;20(1):29-38. doi: 10.1142/S0129065710002218.
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.
本文专注于一种新的用于非线性系统的离散时间降阶神经网络观测器，其模型被假定为未知。该神经网络观测器在存在外部和内部不确定性时具有鲁棒性。所提出的方案基于使用扩展卡尔曼滤波器 (EKF) 算法训练的离散时间递归高阶神经网络 (RHONN)，并采用并行配置。这项工作包括基于 Lyapunov 方法对估计误差进行稳定性证明；为了说明适用性，包括了对非线性振荡器的仿真结果。
