摘要
在动态神经网络及扩展卡尔曼滤波算法的基础上，提出了对非线性随机动态系统进行学习建模的迭代算法．用这种方法对非线性随机系统建模，可以获得更准确的系统模型，并可对非线性随机系统进行状态估计．
A modeling method for nonlinear stochastics dynamic system(NSDS) based on neural network and extended Kalman filter(EKF) is presented. Using this method, the contaminated data by noise can be filtered by EKF. A dynamic neural network(DNN) which is a good approximation to the deterministic part of the NSDS can be obtained. Meanwhile the DNN can be used as a state estimator for the NSDS. In the end of the paper a simulation is shown.
出处
《北京航空航天大学学报》
EI
CAS
CSCD
北大核心
1996年第6期682-686,共5页
Journal of Beijing University of Aeronautics and Astronautics
关键词
神经网络
非线性系统
卡尔曼滤波
neural networks
non linear systems
Kalman filtering
