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An energy minimization approach to vector hysteresis modeling in objects having arbitrary shapes

机译：在具有任意形状的对象中进行矢量滞后建模的能量最小化方法

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摘要

It is known that proper modeling and simulation of vector hysteresis is crucial to the precise design and/or performance estimation of electric power devices and magnetic recording processes. Within this context, only computationally efficient vector hysteresis models may be practically utilized in numerical field computation methodologies which are essential for handling complicated device geometries and excitation schemes. Recently, substantial efforts, focusing on the efficiency enhancement of vector hysteresis models, have been reported [1, 2]. Moreover, discrete Hopfield neural networks (DHNN) have been successfully configured to construct vector hysteresis models [3]. This paper presents a novel DHNN approach to model vector hysteresis in triangular regions, which are the most commonly used discretization sub-domain components in field computation engines.
机译：众所周知，矢量滞后的正确建模和仿真对于电力设备和磁记录过程的精确设计和/或性能估计至关重要。在这种情况下，在数值场计算方法中只能实际使用计算效率高的矢量磁滞模型，这对于处理复杂的器件几何形状和激励方案必不可少。最近，已经报道了大量的工作，着重于矢量滞后模型的效率提高[1，2]。此外，离散Hopfield神经网络（DHNN）已成功配置为构造矢量磁滞模型[3]。本文提出了一种新颖的DHNN方法来对三角形区域中的矢量滞后建模，这是现场计算引擎中最常用的离散化子域组件。

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