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Design and Characterization Methodology for Efficient Wide Range Tunable MEMS Filters
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基于人工智能的多目标天线参数调节,使用可变忠实度机器学习.

Slawomir Koziel1,2, Anna Pietrenko-Dabrowska3, Stanislaw Szczepanski2

  • 1Engineering Optimization & Modeling Center, Reykjavik University, 102, Reykjavik, Iceland.

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概括

本研究介绍了一种人工智能 (AI) 方法,用于天线设计的多目标优化 (MO),显著降低计算成本. 人工智能方法可以节省大量成本,使先进的天线优化更容易获得.

关键词:
天线工程天线工程人工智能,可变忠实度模拟.基于人工智能的设计.机器学习 机器学习神经网络的神经网络的神经网络代理模拟代理模拟

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科学领域:

  • 电气工程 电气工程
  • 计算电磁学 计算机电磁学
  • 天线理论天线理论

背景情况:

  • 由于电磁 (EM) 模拟,天线设计中的多目标优化 (MO) 在计算上是密集的.
  • 对于复杂的天线优化任务,传统的算法往往是不可行的.
  • 替代模型和软计算提供了潜在的成本降低策略.

研究的目的:

  • 为天线多目标优化 (MO) 引入基于人工智能 (AI) 的创新方法.
  • 为了减少与天线设计中的传统MO方法相关的计算成本.
  • 提高天线优化的效率和可行性,特别是在计算预算限制下.

主要方法:

  • 利用机器学习 (ML) 程序,使用人工神经网络 (ANN) 模型为天线MO.
  • 集成的帕雷托排名和多目标进化算法来生成填充向量.
  • 采用多分辨率的电磁模拟和代的元模型改进.
  • 整合了所有充填点的全波模拟结果,以改进代用模型.

主要成果:

  • 基于人工智能的方法显示了显著的成本降低,平均约有200个高保真EM分析.
  • 通过可变忠实度建模实现了40%的相对加快速度,与一次性方法相比,节省了近90%的成本.
  • 验证了四个平面天线设计的方法,包括宽带断和准Yagi天线.
  • 对比实验证实了框架的可靠性,而不会影响计算效率.

结论:

  • 拟议的人工智能驱动的方法为天线多目标优化提供了可行和高效的替代方案.
  • 这种方法有效地解决了天线设计中计算预算的关键约束.
  • 机器学习,代用建模和多分辨率模拟的集成显著提高了优化过程.