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
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