基于MEA-BP神经网络的开关磁阻电机静态电磁特性建模

建立精确的开关磁阻电机（SRM）模型对于改善SRM的性能和控制效果有着重要的影响。针对SRM运行时磁路的高度饱和和严重非线性问题，提出了基于思维进化算法（MEA）优化的反向传播(BP)神经网络算法的SRM非线性模型。利用ANSYS Maxwell软件建立了四相8/6极SRM模型并进行有限元计算，通过仿真和试验值的对比验证了该模型的精度比未经MEA优化的BP神经网络模型更高，可以更好地反映SRM运行时的磁链特性和转矩特性，且具有较好的泛化能力。

Modeling of Static Electromagnetic Characteristics of Switched Reluctance Motor Based on MEA-BP Neural Network

Establishing an accurate switched reluctance motor (SRM) model has an important impact on improving the performance and control effect of SRM. For the problems of high saturation and serious nonlinearity of magnetic circuit during SRM operation, an SRM nonlinear model based on back propagation (BP) neural network algorithm optimized by mind evolutionary algorithm (MEA) is proposed. A four-phase 8/6-pole SRM model is established by using ANSYS Maxwell software and the finite element calculation is carried out. It is verified through the comparison between the simulated value and the experimental value that the model has higher accuracy than the BP neural network model without MEA optimization. It can better reflect the flux linkage and torque characteristics during SRM operation, and has better generalization ability.