Departament of Electrical Engineering, Cinvestav del IPN, Unidad Guadalajara, Av. del Bosque 1145, Colonia el Bajío, Zapopan, 45019, Jalisco, México.
Int J Neural Syst. 2014 Feb;24(1):1450011. doi: 10.1142/S0129065714500117. Epub 2013 Dec 11.
In this paper, a reduced order neural observer (RONO) with a time-varying learning rate is proposed. The proposed scheme is based on a discrete-time recurrent high order neural network (RHONN) trained with an extended Kalman filter (EKF)-based algorithm. A time-varying learning rate is designed in order to improve the learning of the neuronal network in presence of disturbances and parameter variations. This work includes the stability proof of the time-varying learning. The applicability of the developed observer is illustrated via simulations for a nonlinear anaerobic digestion process.
本文提出了一种具有时变学习率的降阶神经网络观测器（RONO）。所提出的方案基于一个离散时间递归高阶神经网络（RHONN），该网络采用基于扩展卡尔曼滤波器（EKF）的算法进行训练。设计了时变学习率，以提高神经元网络在存在干扰和参数变化时的学习能力。本文包括时变学习稳定性的证明。通过对非线性厌氧消化过程的仿真，说明了所开发观测器的适用性。
