基于BP神经网络的开关磁阻电机转矩模型研究

ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501) Pub Date : 2001-08-18 DOI:10.1109/ICEMS.2001.971854

Jia Haolai, Chen Yan, Wang Zhenmin

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引用次数: 1

摘要

基于BP神经网络在非线性系统建模和控制方面的优点，建立了开关磁阻电机的神经网络转矩模型。仿真结果表明，基于bp神经网络的转矩模型比局部线性化转矩模型更具鲁棒性和自适应性，能更准确地反映SRM的工作特性。

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A torque model study of switched reluctance motor using BP neural network

In the paper, a neural network torque model of a switched reluctance motor (SRM) is established, based on the merits of the backpropagation (BP) neural network in the area of modeling and control of nonlinear systems. The simulation results show that the torque model based on BP-neural network is more robust and adaptive, and can reflect the working properties of SRM more accuracy than the local linearization torque model.

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ICEMS'2001. Proceedings of the Fifth International Conference on Electrical Machines and Systems (IEEE Cat. No.01EX501)

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