Modeling of switched reluctance motors based on optimized BP neural networks with parallel chaotic search

Author

Cheng, Yong ; Lin, Hui

Author_Institution

Autom. Coll., Northwestern Polytech. Univ., Xi´´an, China

Volume

1

fYear

2010

fDate

6-7 March 2010

Firstpage

153

Lastpage

156

Abstract

Precise modeling of switched reluctant motor (SRM) is important of switched reluctant motor driving system. In the article, modeling of SRM by a BP neural network with parallel chaotic search (PCS) is presented firstly. Here parallel chaotic search is proposed to optimize vectors of weight and threshold. Modified BP neural network has been improved in convergence, generalizing and network scale for real time control. Based on the results of simulation, the nonlinear modeling of SRM has performed better, which has faster convergence and improved in efficiency.

Keywords

backpropagation; chaos; neural nets; power engineering computing; reluctance motors; search problems; SRM nonlinear modeling; optimized BP neural networks; parallel chaotic search; real time control; switched reluctant motor driving system; Chaos; Couplings; Mathematical model; Neural networks; Personal communication networks; Reluctance machines; Reluctance motors; Robotics and automation; Torque; Voltage; BP neural network; optimize; parallel chaotic search; switched reluctant motor;

fLanguage

English

Publisher

ieee

Conference_Titel

Informatics in Control, Automation and Robotics (CAR), 2010 2nd International Asia Conference on

Conference_Location

Wuhan

ISSN

1948-3414

Print_ISBN

978-1-4244-5192-0

Electronic_ISBN

1948-3414

Type

conf

DOI

10.1109/CAR.2010.5456882

Filename

5456882