磁MEMS振荡的里叶近似:神经网络空间处理
Piotr Skrzypacz1, Arman Bolatov1, Andrzej Dziedzic2
1Department of Mathematics, School of Sciences and Humanities, Nazarbayev University, Astana 010000, Kazakhstan.
Micromachines
|December 31, 2025
概括
这项研究分析了带电导体的磁性微型电机系统 (magMEMS). 一种新的神经网络方法增强了对保守的MEMS振荡器的解决方案,有助于magMEMS设计.
科学领域:
- 物理 物理学 物理
- 电气工程 电气工程
- 机械工程 机械工程
背景情况:
- 磁性微电机系统 (magMEMS) 在各种应用中至关重要.
- 建模保守的MEMS振荡器与奇数项,如磁力,提出了挑战.
研究的目的:
- 为了解决带电导体的magMEMS模型的初始值问题.
- 通过使用神经网络来提高周期性解决方案的福里埃近似值,以提高准确性.
主要方法:
- 开发了一种用于带电导体的magMEMS模型,采用保守振荡器.
- 应用神经网络空间处理到周期性解决方案的富里埃近似.
- 研究的极限情况 (几何参数接近零) 和一般情况.
主要成果:
- 增强的里埃近似方法为magMEMS模型提供了准确的解决方案.
- 该方法与高精度的数值解决方案进行验证.
- 通过实验结果验证确定了局限性.
结论:
- 提出的方法有效地模拟了magMEMS振荡器的磁力.
- 这些发现对于优化magMEMS设备的设计具有价值.
- 神经网络集成为分析复杂的MEMS系统提供了强大的工具.
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