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 :
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