An anisotropic magnetostriction model based on BP neural network combining Levenberg-Marquardt algorithm and particle swarm optimization

Issue title: 14th International Workshop on 1&2 Dimensional Magnetic Measurement and Testing (2016)

Guest editors: Zhiguang Cheng, Jianguo Zhu, Yongjian Li and Johannes Sievert

Article type: Research Article

Authors: Zhou, Hang | Zhang, Yanli^* | Zhang, Dianhai | Ren, Ziyan | Xie, Dexin

Affiliations: School of Electrical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China

Correspondence: [*] Corresponding author: Yanli Zhang, School of Electrical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China. Tel.: +86 139 4026 9519; E-mail: ylzhang@sut.edu.cn.

Abstract: In this paper, the dynamic magnetostriction of the electrical steel sheet is measured by a triaxial strain gauge based on a single-sheet tester under the alternating magnetic flux condition. The anisotropic magnetostriction property is analyzed, and an improved back propagation neural network assisted model is applied to model the anisotropic principal strain of magnetostriction along different excitation directions. To keep the evaluation cost at an acceptable level, the Levenberg-Marquardt algorithm combined with particle swarm optimization is developed. Finally, the proposed model is verified by comparing the measured magnetostriction and computed one.

Keywords: Anisotropic magnetostriction, electrical steel, BP neural network, alternating magnetization

DOI: 10.3233/JAE-172273

Journal: International Journal of Applied Electromagnetics and Mechanics, vol. 55, no. S1, pp. 193-201, 2017