DocumentCode
2307611
Title
Wavelet Neural Network Based on SSUKF and its Applications in Aerodynamic Force Modeling for Flight Vehicle
Author
Gan Xusheng ; Duanmu Jingshun ; Cong Wei
Author_Institution
XiJing Coll., Xi´an, China
Volume
3
fYear
2010
fDate
13-14 March 2010
Firstpage
1087
Lastpage
1090
Abstract
To overcome the shortcomings of traditional Wavelet Neural Network (WNN), a WNN algorithm based on modified Unscented Kalman Filter (UKF) is proposed. The algorithm uses a UKF based on Spherical Simplex sigma-point (SSUKF) to estimate the WNN parameters, which can improve the learning capability of WNN. The aerodynamic force modeling experiment for flight vehicle indicate that, compared with BP, EKF and UKF, SSUKF for the WNN training has a better ability with features of convergence, precision and calculation, and is also a good method for aerodynamic force modeling for flight vehicle.
Keywords
Kalman filters; aerodynamics; aircraft; neural nets; parameter estimation; wavelet transforms; SSUKF; WNN algorithm; aerodynamic force modeling; flight vehicle; parameter estimation; spherical simplex sigma-point; unscented Kalman filter; wavelet neural network; Aerodynamics; Aerospace engineering; Automotive engineering; Convergence; Educational institutions; Equations; Force measurement; Neural networks; Vehicles; Wavelet transforms; Aerodynamic Force; Kalman Filter; Unscented Transformation; Wavelet Neural Network;
fLanguage
English
Publisher
ieee
Conference_Titel
Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
Conference_Location
Changsha City
Print_ISBN
978-1-4244-5001-5
Electronic_ISBN
978-1-4244-5739-7
Type
conf
DOI
10.1109/ICMTMA.2010.623
Filename
5460277
Link To Document