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

fYear

2011

fDate

9-11 Sept. 2011

Firstpage

44

Lastpage

47

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Electronics, Communications and Control (ICECC), 2011 International Conference on

Conference_Location

Ningbo

Print_ISBN

978-1-4577-0320-1

Type

conf

DOI

10.1109/ICECC.2011.6067619

Filename

6067619