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A Neural Network and NSGA-II Based Multi-objective Optimization Design Method for Permanent Magnet Synchronous Machine

机译:基于神经网络和NSGA-II的永磁同步电机多目标优化设计方法

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This paper presents a multi-objective optimization design method for permanent magnet synchronous machine (PMSM) by using back propagation neural network combined with non-dominated sorting genetic algorithm II (NSGA-II). First, neural network is used to map out the nonlinear relationship between the structural parameters of the machine and the output performances, such as average torque and torque ripple. Then NSGA II is implemented to find out the Pareto-optimal front. According to the Pareto-optimal front, optimal structure parameters of the PMSM for the best output optimization results are selected.
机译:本文提出了一种利用反向传播神经网络与非支配排序遗传算法II(NSGA-II)相结合的永磁同步电机(PMSM)多目标优化设计方法。首先,使用神经网络绘制出机器的结构参数与输出性能(例如平均转矩和转矩脉动)之间的非线性关系。然后实施NSGA II以找出帕累托最优前沿。根据帕累托最优前沿,选择用于最佳输出优化结果的PMSM最佳结构参数。

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