Title :
A modeling method of SRM based on RBF neural networks
Author :
Qi, Shufen ; Kong, Hui
Author_Institution :
Coll. of Autom. & Electron. Eng., Qingdao Univ. of Sci. & Technol., Qingdao, China
Abstract :
This paper presents a modeling method of Switched Reluctance Motor (SRM) based on the Radial Basis Function (RBF) Neural Networks. By analysing measuring data and nonlinear characteristics of SRM, the modeling of SRM is designed with Gaussion Function. The simulated results show that the proposed model has better capability of generalization and correctly represents the characteristics of SRM compared with traditional method of local linearization or BP Neural Networks, which is more significative to real-time control for SRM.
Keywords :
electric machine analysis computing; machine theory; radial basis function networks; reluctance motor drives; BP neural network; Gaussion function; RBF neural network; SRM drive modeling method; radial basis function neural network; real-time control; switched reluctance motor modeling method; Couplings; Mathematical model; Neural networks; Reluctance motors; Rotors; Training; Modeling; RBF Neural Networks; SRM;
Conference_Titel :
Electronics, Communications and Control (ICECC), 2011 International Conference on
Conference_Location :
Ningbo
Print_ISBN :
978-1-4577-0320-1
DOI :
10.1109/ICECC.2011.6067619
