Multi-objective Optimization Design of PMASynRM Based on RBF Neural Network. Issue 1 (1st January 2022)

• Record Type: Journal Article
• Authors: Shuai, Kang; Li, Zequan; Zhou, Libing; Wang, Jin
• Is Part Of: Journal of physics. Volume 2183:Issue 1(2022)
• Publication Date: 2022-01-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2183/1/012013
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: This paper presents an optimized design method of PMASynRM based on the RBF neural network. Firstly, Sobol sensitivity analysis method is used to analyze the mutual influence of the parameter variables of the motor. Then, in order to establish the surrogate model of the finite element model, the samples are obtained by the Latin hypercube sampling method to train the RBF neural network, and the NSGA-? algorithm is used for multi-objective optimization based on the trained RBF neural network. Finally, the optimization scheme is verified by the results of finite element analysis that the proposed method can provide an optimal design.