Article ID Journal Published Year Pages File Type
5490943 Journal of Magnetism and Magnetic Materials 2017 6 Pages PDF
Abstract
Field computation in media exhibiting hysteresis is crucial to a variety of applications such as magnetic recording processes and accurate determination of core losses in power devices. Recently, Hopfield neural networks (HNN) have been successfully configured to construct scalar and vector hysteresis models. This paper presents an efficient hysteresis modeling methodology and its implementation in field computation applications. The methodology is based on the application of the integral equation approach on discretized triangular magnetic sub-regions. Within every triangular sub-region, hysteresis properties are realized using a 3-node HNN. Details of the approach and sample computation results are given in the paper.
Related Topics
Physical Sciences and Engineering Physics and Astronomy Condensed Matter Physics
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