摘要
针对传统小波网络算法的不足,提出一种基于改进无迹Kalman滤波(UKF)的小波网络算法.该算法使用一种基于简化球形分布Sigma点的UKF(SSUKF)来训练小波网络的参数,以提高小波网络的学习性能和训练质量.飞行器气动力建模算例表明,相对于BP算法和EKF算法,SSUKF算法训练的小波网络收敛速度更快,估计精度更高,计算量更小.同时也为飞行器的气动力建模提供了一种有效可行的手段.
For the shortcoming of traditional wavelet neural network（WNN）,a WNN algorithm based on modified unscented Kalman filter（UKF） is proposed.The algorithm uses an UKF based on Sigma point of simplex spherical distribution（SSUKF）to train the parameters of WNN,which can improve the learning ability and training quality of WNN.The experiment results on aerodynamic modeling of fiight vehicle show that,compared with BP and extended Kalman filter（EKF）,the WNN trained by SSUKF algorithm has a better ability with features of convergence,precision and calculation,and is also a good method for aerodynamic modeling of fiight vehicle.
出处
《控制与决策》
EI
CSCD
北大核心
2011年第2期187-190,195,共5页
Control and Decision
关键词
小波网络
KALMAN滤波
气动力
飞行器
wavelet neural network
Kalman filter
aerodynamic force
fiight vehicle
