• DocumentCode
    482951
  • Title

    The model of nonlinear radial force in switched reluctance motor based on radial basis function neuron network

  • Author

    Weibing, Wang ; Honghua, Wang ; Jiyong, Li

  • Author_Institution
    Electr. Eng. Inst. of Hohai Univ., Nanjing
  • fYear
    2008
  • fDate
    17-20 Oct. 2008
  • Firstpage
    3411
  • Lastpage
    3413
  • Abstract
    Based on radial basis function neuron network (RBFNN), the model of nonlinear radial force in switched reluctance motor(SRM) is constructed in this paper. Training samples for RBFNN are obtained from the calculation results of a prototype SRM(8/6) with finite element method(FEM). Training algorithm is a hybrid method combining nearest neighbor clustering with steepest gradient descent. The simulation comparison results of the RBFNN with the hybrid training algorithm in this paper and the back propagation neuron network (BPNN) with Levenberg-Marquardt training algorithm in MATLAB validates the superiority of the RBFNN.
  • Keywords
    finite element analysis; gradient methods; pattern clustering; power engineering computing; radial basis function networks; reluctance motors; finite element method; hybrid training algorithm; nearest neighbor clustering; nonlinear radial force; radial basis function neuron network; steepest gradient descent; switched reluctance motor; Clustering algorithms; Electromagnetic interference; Magnetic flux; Mathematical model; Nearest neighbor searches; Neurons; Prototypes; Reluctance machines; Reluctance motors; Stators;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical Machines and Systems, 2008. ICEMS 2008. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-3826-6
  • Electronic_ISBN
    978-7-5062-9221-4
  • Type

    conf

  • Filename
    4771355