使用人工智能方法和反向神经网络替代品减少天线阵列的相互合
Saeed Roshani1, Slawomir Koziel2,3, Salah I Yahya4,5
1Department of Electrical Engineering, Kermanshah Branch, Islamic Azad University, Kermanshah 67771, Iran.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一种使用定制共振器和反向替代模型的新方法,以显著减少天线阵列合. 这种方法实现了超过37dB的隔离改进,提高了天线性能.
科学领域:
- 电磁学和波浪理论
- 天线阵列设计设计
- 超材料和共振器的应用.
背景情况:
- 天线阵列的相互合降低了整体系统性能.
- 传统的合减速方法可能是复杂和耗时的.
- 优化共振器参数用于合抑制需要高效的建模技术.
研究的目的:
- 介绍一种用于减少天线阵列相互合的新方法.
- 引入使用定制设计的渐进阻抗共振器来增强隔离.
- 展示反向人工神经网络 (ANN) 替代模型的应用,以优化共振器几何.
主要方法:
- 设计和制造两个在2.45 GHz工作的补丁天线单元.
- 在天线之间集成一个分级阻抗共振器,以减轻合.
- 使用电磁仿真数据,开发一个经过粒子群优化 (PSO) 训练的反向ANN替代模型.
- 从目标S参数值直接获得最佳的共振器尺寸.
主要成果:
- 在2.45GHz时实现了-46.2dB的隔离,这与传统设置相比是显著的改进.
- 在运行频率下,在天线之间的表面电流有显著的取消.
- 验证了反向ANN替代模型在优化共振器参数方面的有效性.
- 展示了由于代用模型的加速设计过程,避免了直接的电磁优化.
结论:
- 拟议的方法有效地减少了使用定制共振器和反向替代模型的天线阵列的相互合.
- 反向ANN替代模型提供了一个高效和加速的途径,以优化共振器设计,以提高天线隔离.
- 实现的隔离水平代表了天线阵列性能的重大进步.
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