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Identification Parameters with Neural Network for Preisach Hysteresis Model

机译:具有Preisach滞后模型的神经网络识别参数

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The description of hysteresis is one of the classical problems in magnetic materials. The progress in its solution determines the reliability of modeling and the quality of design of a wide range of devices, the proposed approach has been applied to model the behavior of many samples and the results show the robustness and efficiency of Neural Network to model the phenomenon of hysteresis loop. The goal of this study is to optimize the parameters of hysteresis Loop by Preisach model with the Neural Network, the method developed is based on an analysis of two distribution functions. The modified Lorentzian function and Gaussian function have been analyzed. The implemented software and performances of the distributions are presented.
机译:滞后的描述是磁性材料中的古典问题之一。 其解决方案中的进展决定了建模的可靠性和各种设备的设计的质量,所提出的方法已应用于模拟许多样本的行为,结果表明神经网络模拟现象的鲁棒性和效率 滞后环。 本研究的目的是通过具有神经网络的Preisach模型优化滞后环的参数,该方法开发的方法是基于两个分布函数的分析。 已经分析了改进的Lorentzian函数和高斯功能。 提出了已实现的软件和分布的性能。

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