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Two-dimensional magnetic modeling of ferromagnetic materials by using a neural networks based hybrid approach

机译：基于神经网络的混合方法对铁磁材料进行二维磁性建模

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This paper presents a hybrid neural network approach to model magnetic hysteresis at macro-magnetic scale. That approach aims to be coupled together with numerical treatments of magnetic hysteresis such as FEM numerical solvers of the Maxwell's equations in time domain, as in case of the non-linear dynamic analysis of electrical machines, and other similar devices, allowing a complete computer simulation with acceptable run times. The proposed Hybrid Neural System consists of four inputs representing the magnetic induction and magnetic field components at each time step and it is trained by 2D and scalar measurements performed on the magnetic material to be modeled. The magnetic induction B is assumed as entry point and the output of the Hybrid Neural System returns the predicted value of the field H at the same time step. Within the Hybrid Neural System, a suitably trained neural network is used for predicting the hysteretic behavior of the material to be modeled. Validations with experimental tests and simulations for symmetric, non-symmetric and minor loops are presented. (C) 2015 Elsevier B.V. All rights reserved.
机译：本文提出了一种混合神经网络方法，用于在宏观磁性尺度上建模磁滞。该方法旨在与磁滞的数值处理（例如时域麦克斯韦方程的FEM数值求解器）结合在一起，例如在电机和其他类似设备的非线性动态分析的情况下，可以进行完整的计算机仿真具有可接受的运行时间。拟议的混合神经系统由代表每个时间步长的磁感应和磁场分量的四个输入组成，并通过对要建模的磁性材料执行的2D和标量测量进行训练。假定磁感应B为入口点，并且混合神经系统的输出在同一时间步长返回磁场H的预测值。在混合神经系统中，使用经过适当训练的神经网络来预测要建模的材料的磁滞行为。提出了针对对称，非对称和次循环的实验测试和仿真验证。 （C）2015 Elsevier B.V.保留所有权利。

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