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开发和验证一种基于ANN的方法,用于SAW共振器的温度依赖等效电路建模
Miloš Radojković1, Giovanni Gugliandolo2, Mariangela Latino2
1Faculty of Electronic Engineering, University of Niš, 18000 Niš, Serbia.
Micromachines
|May 27, 2023
概括
这项研究引入了一种用于模拟表面声波 (SAW) 共振器的新方法. 人工神经网络 (ANN) 用于预测温度依赖的行为,增强SAW共振器模拟.
科学领域:
- 电气工程 电气工程
- 材料科学 材料科学 材料科学
- 声学 声学 在声学方面
背景情况:
- 表面声波 (SAW) 共振器是射频系统中的关键组件.
- 在不同温度下精确建模SAW共振器的行为对于设备的性能至关重要.
- 传统的建模方法可能无法完全捕捉复杂的温度依赖性.
研究的目的:
- 开发SAW共振器的新型,温度依赖型号.
- 将人工神经网络 (ANN) 与一次性元素等效电路模型集成.
- 为了在广泛的温度范围内实现SAW设备的精确射频特征模拟.
主要方法:
- 一个块元素等效电路模型与人工神经网络 (ANN) 结合起来.
- 使用ANN来建模等效电路参数 (ECP) 的温度依赖性.
- 用423.22 MHz SAW装置的散射参数测量用于0°C至100°C的验证.
主要成果:
- 一个基于ANN的模型准确地预测了SAW共振器的温度依赖行为.
- 该模型成功地模拟了在测试温度范围 (0°C~100°C) 内的射频特征.
- 基于ANN的模型的准确性与原始等效电路模型可比.
结论:
- 提出的基于ANN的方法为温度依赖的SAW共振器建模提供了一种有效的方法.
- 这种模型消除了对模拟的重复测量或同等电路提取的需求.
- 开发的模型增强了SAW设备在不同热条件下的模拟能力.
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