太赫兹科学与电子信息学报2025，Vol.23Issue(1)：61-65,5.DOI:10.11805/TKYDA2024404
基于KNN和ANN算法的微带天线尺寸优化方法
Microstrip antenna size optimization method based on KNN and ANN algorithms
摘要
Abstract
A microstrip antenna size optimization method based on K-Nearest Neighbors(KNN)and Artificial Neural Network(ANN)algorithms is proposed to solve the problem of high optimization complexity of traditional antennas.By analyzing the surface current distribution of the antenna,high-sensitivity parameters are set as variables,while low-sensitivity parameters are set as constants.The KNN algorithm and ANN algorithm are then utilized to optimize the size parameters of the antenna,ultimately enhancing broadband performance.To validate the effectiveness of the optimization algorithms,two antennas were fabricated and tested.The results indicate that compared to traditional antenna design methods,the KNN and ANN algorithms increase the impedance bandwidth by 20.8%and 18.4%,respectively.Although the ANN algorithm requires longer training time,it demonstrates significant improvements in impedance matching across multiple frequency bands.关键词
K-最近邻(KNN)/人工神经网络(ANN)/机器学习/尺寸优化/微带天线Key words
K-Nearest Neighbors(KNN)/Artificial Neural Network(ANN)/machine learning/optimization design/microstrip antenna分类
信息技术与安全科学引用本文复制引用
窦江玲,李聃,宋健,王青旺,沈韬..基于KNN和ANN算法的微带天线尺寸优化方法[J].太赫兹科学与电子信息学报,2025,23(1):61-65,5.基金项目
国家自然科学基金资助项目(61971208) （61971208）
云南省万人计划青年拔尖人才资助项目(201873) （201873）
云南省基础研究计划资助项目(202401AT070351 （）
202301AV070003) （）
云南省计算机技术应用重点实验室开放基金资助项目(2022202) （2022202）
