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:

为解决传统天线优化复杂度较高的问题，提出一种基于K-最近邻(KNN)算法和人工神经网络(ANN)算法的微带天线尺寸优化方法。该方法通过分析天线表面电流分布，将高敏感度参数设置为变量，低敏感度参数设置为常量，利用KNN和ANN对天线的尺寸参数进行优化，最终实现宽带性能的提升。为验证优化算法的有效性，加工了两款天线进行测试。结果表明，与传统天线设计方法相比，KNN和ANN算法使天线的阻抗带宽分别提高了20.8%和18.4%。其中，ANN算法在训练阶段耗时较长，但其阻抗匹配特性在多个频段上表现出显著改进。

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.