摘要
为加快天线建模优化速度,提出了一种改进的一维卷积神经网络(1D-MCNN)模型。此一维神经网络的卷积核大小为2,将ReLU函数作为激活函数降低梯度弥散;利用Adam优化器与dropout技术结合,提高模型的特征学习能力和非线性函数逼近能力。本文使用1D-MCNN模型对超宽带微带单极子天线几何参数建模,以天线的8个几何参数作为特征输入,对天线的回波损耗值进行预测。实验表明,本文所提1D-MCNN模型与深层MLP网络模型、MLP网络模型、RBF神经网络模型相比,回波损耗值的平均误差分别减小了1.95%,120.27%,125.71%,拥有更高的准确度,预测能力更强,对优化超宽带天线建模可行且性能具有一定优越性。
To speed up the optimization of antenna modeling,an improved one-dimensional convolutional neural network(1D-MCNN)model is proposed.The convolution kernel size of this one-dimensional neural network is 2,and the ReLU function is used as the activation function to reduce the gradient dispersion.The Adam optimizer is combined with dropout technology to improve the feature learning ability and nonlinear function approximation ability of the model.In this paper,the 1D-MCNN model is used to model the geometric parameters of the ultra-wideband microstrip monopole antenna.The eight geometric parameters of the antenna are used as feature inputs to predict the return loss value of the antenna.Experiments show that compared with the deep MLP network model,MLP network model,and RBF neural network model,the average error of the return loss value of the 1D-MCNN model proposed in this paper is reduced by 1.95%,120.27%,and 125.71%respectively.It has higher accuracy and stronger prediction ability.It is feasible to optimize the modeling of ultra-wideband antennas and has certain advantages.
作者
南敬昌
孙雯雯
杜有益
王明寰
Nan Jingchang;Sun Wenwen;Du Youyi;Wang Minghuan(School of Electronics and Information Engineering,Liaoning Technical University,Huludao 125105,China;School of Electronic Engineering and Photoelectric Technology,Nanjing University of Science and Technology,Nanjing 210000,China)
出处
《电子测量与仪器学报》
CSCD
北大核心
2023年第2期204-210,共7页
Journal of Electronic Measurement and Instrumentation
基金
国家自然科学基金(61971210)
企业合作课题:射频LDMOS功放器件研究测试(21-2-32)项目资助
