• 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