基于深度学习的太赫兹编码频率选择表面优化设计
Optimized Design of Terahertz Coding Frequency Selection Surface Based on Deep Learning
周攀 1巩蕾 1阳志强 1王利国 1杨利红 1李瑶 1王海斌 1于洁1
作者信息
- 1. 西安工业大学光电工程学院,陕西 西安 710021
- 折叠
摘要
针对太赫兹频率选择表面(FSS)单元结构复杂、参数繁多、优化耗时等问题,基于卷积神经网络(CNN)并结合遗传算法,提出一种FSS智能化融合设计方法,并将其应用于典型滤波FSS的性能优化设计.将FSS周期单元拓扑编码为16×16的"0/1"旋转对称序列,收集26000组0.5～3 THz的透反射光谱作为数据集,利用19层CNN实现光谱预测,对测试集的平均绝对误差低至0.06.利用预测光谱与目标光谱的差值,给出各类典型FSS设计的通用目标函数,结合遗传算法,设计实现了带宽为0.1 THz的单频带通和带阻FSS、带宽为0.5 THz的单频带通和带阻FSS、带宽为0.2 THz的双频带通FSS,且均具有良好极化稳定性.计算结果表明,通过优化设计FSS的拓扑编码,可以简洁高效地实现各类典型带通带阻FSS.
Abstract
This study proposes an intelligent integration-design method to address the problems of complex structure,numerous parameters,and time-consuming optimization of terahertz frequency selective surface(FSS)cells.The method is based on convolutional neural network(CNN)combined with genetic algorithm,and is applied to the performance optimization design of typical filtered FSS.The FSS periodic cell topology is encoded as a 16×16"0/1"rotationally symmetric sequence,and 26,000 groups of transmission and reflectance spectra in the range of 0.5‒3 THz are collected as the dataset.A 19-layer CNN is used to obtain the spectral prediction with an average absolute error as low as 0.06 on the test set.The difference between the predicted and the target spectra is used to give a generalized objective function for the design of various typical FSS cells.Combined with the genetic algorithm,a single-frequency bandpass and bandstop FSSs with a bandwidth of 0.1 THz,a single-frequency bandpass and bandstop FSSs with a bandwidth of 0.5 THz,and a dual-frequency bandpassFSS with a bandwidth of 0.2 THz are designed and implemented,with good polarization stability for all of them.Computational results show that various typical bandpass and bandstop FSS cells can be realized concisely and efficiently by optimizing their topological coding.
关键词
频率选择表面/拓扑编码/卷积神经网络/遗传算法Key words
frequency selective surface/topological coding/convolutional neural network/genetic algorithm引用本文复制引用
基金项目
国家自然科学基金(62071359)
国家自然科学基金(62001364)
陕西省教育厅重点科研计划(20JS059)
陕西省高等学校青年创新团队项目(K20220184)
西安工业大学校长基金面上培育项目(XGPY200206)
出版年
2024
NETL
NSTL
万方数据