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
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;
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