通信学报2026，Vol.47Issue(1)：267-278,12.DOI:10.11959/j.issn.1000−436x.2026013
基于改进随机森林算法与多尺度卷积神经网络的频率选择表面敏捷设计
Integrating improved random forest algorithm and multi-scale convolutional neural network for agile design of frequency selective surface
摘要
Abstract
To address the issues of large prediction deviation and high dataset cost in traditional frequency selective sur-face(FSS)design combined with neural networks,an agile FSS design framework based on improved random forest(RF)and multi-scale convolutional neural network(MS-CNN)was proposed.In the framework,the improved RF opti-mized the sampling strategy through electromagnetic characteristic splitting criteria and multi-feature interaction evalua-tion to construct a high-quality dataset—only 1 157 samples were needed to achieve a prediction mean squared error(MSE)<2.0,reducing the sample size by 61％compared with traditional sampling.The MS-CNN used 3×1,5×1,and 7×1 multi-scale convolution kernels to extract electromagnetic response features,and combined with a frequency gradient loss function;the prediction MSE of TE/TM dual-polarization S21 curves at 0°/70° incident angles was as low as 2.2.With MS-CNN as the prediction agent,reverse design combined with particle swarm optimization(PSO)was conducted to output FSS parameters meeting the requirements:S21≥-1.5 dB in the 25～33 GHz band,stable response at 0°～70° inci-dent angles,and dual-polarization adaptation.The parameters were verified to meet the standards via HFSS,and the model's generalization was validated in the 20～28 GHz band.This framework provides an efficient solution for the agile design of wideband,multi-polarization,and wide-angle FSS.关键词
频率选择表面/随机森林算法/多尺度卷积神经网络/粒子群优化Key words
frequency selective surface/Random forest algorithm/multi-scale convolutional neural network/PSO分类
信息技术与安全科学引用本文复制引用
王义富,杜源,廖广昕,李华萍,任燕飞,黄浩然,蒋伟,郑沈理,郭嘉诚,杜力..基于改进随机森林算法与多尺度卷积神经网络的频率选择表面敏捷设计[J].通信学报,2026,47(1):267-278,12.基金项目
国家重点研发计划基金资助项目(No.2021YFA0717700) （No.2021YFA0717700）
江苏省双创团队基金资助项目(No.JSSCTD202202)The National Key Research and Development Program of China(No.2021YFA0717700),Jiangsu Province Double Creation Team(No.JSSCTD202202) （No.JSSCTD202202）
