Feature selection for determining input parameters in antenna modeling  

作　　者：LIU Zhixian SHAO Wei CHENG Xi OU Haiyan DING Xiao 

机构地区：[1]School of Physics,University of Electronic Science and Technology of China,Chengdu 611731,China [2]School of Computer and Information Engineering,Xinjiang Agricultural University,Urumqi 830052,China

出　　处：《Journal of Systems Engineering and Electronics》2025年第1期15-23,共9页系统工程与电子技术(英文版)

基　　金：National Natural Science Foundation of China(62161048);Sichuan Science and Technology Program(2022NSFSC0547,2022ZYD0109)。

摘　　要：In this paper,a feature selection method for determining input parameters in antenna modeling is proposed.In antenna modeling,the input feature of artificial neural network(ANN)is geometric parameters.The selection criteria contain correlation and sensitivity between the geometric parameter and the electromagnetic(EM)response.Maximal information coefficient(MIC),an exploratory data mining tool,is introduced to evaluate both linear and nonlinear correlations.The EM response range is utilized to evaluate the sensitivity.The wide response range corresponding to varying values of a parameter implies the parameter is highly sensitive and the narrow response range suggests the parameter is insensitive.Only the parameter which is highly correlative and sensitive is selected as the input of ANN,and the sampling space of the model is highly reduced.The modeling of a wideband and circularly polarized antenna is studied as an example to verify the effectiveness of the proposed method.The number of input parameters decreases from8 to 4.The testing errors of|S_(11)|and axis ratio are reduced by8.74%and 8.95%,respectively,compared with the ANN with no feature selection.

关 键 词：antenna modeling artificial neural network(ANN) feature selection maximal information coefficient(MIC) 

分 类 号：TN820[电子电信—信息与通信工程] TP183[自动化与计算机技术—控制理论与控制工程]