现代电子技术2024，Vol.47Issue(19)：14-20,7.DOI:10.16652/j.issn.1004-373x.2024.19.003
基于KNN-XGBOOST堆叠模型在PCB RFID天线阻抗预测的研究
KNN-XGBOOST-based stacked model for PCB RFID antenna impedance prediction
摘要
Abstract
In view of the time-consuming antenna impedance required in the traditional processes of antenna simulation and modeling,an antenna impedance prediction method on the basis of KNN-XGBOOST-based model is proposed.Most of the existing studies focus on the single prediction algorithms.These studies aim to find out the algorithms with better prediction effect by comparative analysis.Initially,a substantial data of PCB(printed circuit board)RFID(radio frequency identification)antenna impedance design is collected with simulation software ANSYS.Subsequently,leveraging eight influential features including antenna length and frequency,a stacked ensemble learning model is constructed on the basis of constructing the base model with algorithms KNN(K-nearest neighbor)and XGBOOST(eXtreme Gradient Boosting),and the meta-model with linear regression.In the experiment,fine-tuning of the model´s hyperparameters is implemented via cross-validation and grid search techniques,so as to ensure that the model can reach the optimal predictive performance.The experimental results demonstrate that the root mean square error(RMSE)of the KNN-XGBOOST-based model is reduced by 30%～70%,and its R-squared(R2)is increased by 10%in comparison with those of the KNN-based model and XGBOOST-based model.When predicting the real and imaginary parts of PCB RFID antenna impedance,the KNN-XGBOOST-based model exhibits higher accuracy rate and lower prediction error,which verifies its application value in the optimization of electromagnetic simulation design.关键词
PCB RFID天线/阻抗预测/KNN算法/XGBOOST算法/融合堆叠/电磁仿真Key words
PCB RFID antenna/impedance prediction/KNN algorithm/XGBOOST algorithm/ensemble stacking/electro-magnetic simulation分类
电子信息工程引用本文复制引用
姜延坤,洪涛,章吉丽..基于KNN-XGBOOST堆叠模型在PCB RFID天线阻抗预测的研究[J].现代电子技术,2024,47(19):14-20,7.基金项目
浙江省基础公益研究计划项目(LGG22E050011) （LGG22E050011）
