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使用反复的神经网络和域限制的被动微波电路的替代建模
Kaustab C Sahu1, Slawomir Koziel2,3, Anna Pietrenko-Dabrowska4
1Engineering Optimization & Modeling Center, Reykjavik University, 101, Reykjavik, Iceland.
Scientific reports
|April 17, 2025
概括
这项研究引入了一种新的循环神经网络 (RNN) 方法,用于准确且具有成本效益的微波电路替代模型. 该方法通过使用小型数据集提高了可靠性和预测能力,优于现有技术.
科学领域:
- 电气工程 电气工程
- 计算电磁学 计算机电磁学
- 机器学习应用 机器学习应用
背景情况:
- 电磁 (EM) 模拟在微波工程中至关重要但昂贵.
- 对于复杂的微波电路来说,准确的替代品建模是具有挑战性的.
- 现有的方法在高维度和广泛的参数范围方面扎.
研究的目的:
- 为微波电路开发一种可靠且具有成本效益的替代模型技术.
- 提高EM模拟替代品的准确性和预测能力.
- 在复杂的设计中解决传统代孕模型的局限性.
主要方法:
- 使用循环神经网络 (RNN) 具有双向长短期记忆 (LSTM) 和门式循环单元 (GRU) 层.
- 在RNN架构中将频率视为一个顺序变量.
- 在超参数调整中使用贝叶斯优化 (BO).
- 实施全局灵敏度分析以减少域名.
主要成果:
- 拟议的基于RNN的替代模型显著提高了可靠性和成本效益.
- 该方法即使在小型训练数据集中也能实现出色的预测能力.
- 在微条纹结构上验证,它显示了与基于内核和其他神经网络方法的竞争性性能.
结论:
- 新的RNN方法为微波电路代用模拟提供了一种优越的解决方案.
- 这种技术有效地降低了计算成本,同时保持了高精度.
- 它为优化复杂的微波电路设计提供了一个强大的工具.
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