深度学习架构神经网络对超宽带天线建模优化  

Deep Learning Architecture and Neural Network Optimization of Ultra-Wideband Antenna Modeling

作　　者：南敬昌[1] 杜有益 王明寰 高明明[1] Nan Jingchang;Du Youyi;Wang Minghuan;Gao Mingming(School of Electronics and Information Engineering,Liaoning Technical University,Huludao 125105,Liaoning,China)

机构地区：[1]辽宁工程技术大学电子与信息工程学院,辽宁葫芦岛125105

出　　处：《激光与光电子学进展》2022年第13期352-358,共7页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(61971210)。

摘　　要：为了加快天线建模优化速度,提出了一种基于深度学习网络架构的新型深度多层感知机(DMLP)网络用于优化超宽带天线,该网络采用阶梯下降形深度全连接层网络,应用Adam优化器自动更新学习率,加快了模型的权值更新。应用drop-out技术对隐含层中的部分神经元进行随机剔除,以防止网络层数过深所导致的过拟合现象的发生。使用DMLP网络对超宽带阶梯形微带单极子天线几何参数进行建模,从天线的8个几何参数中提取特征,对天线的S11值进行预测。实验结果表明,该结构网络与传统多层感知器神经网络、径向基神经网络相比,对S11的预测平均误差分别减小了118.32%和123.76%,拥有更高的预测准确度,拟合速度也有较大提升,通过实验验证了此网络的可行性。To speed up the optimization of antenna modeling,this paper proposes a novel deep multi-layer perceptron(DMLP)network based on deep learning network architecture for optimizing ultra-wideband antenna.The DMLP network uses a step-down,connected-layer deep network,and the Adam optimizer automatically updates the learning rate.Dropout technology is used to remove random neurons in the hidden layer,preventing overfitting due to the deep network layers.This paper uses the DMLP network to model the geometric parameters of the ultra-wideband stepped microstrip monopole antenna,extracts features from the eight geometric parameters of the antenna,and predicts the S11 value of the antenna.The experimental results show that compared with traditional multilayer perceptron and radial-basis-function neural networks,the average prediction error of S11 is reduced by 118.32% and 123.76%,respectively,and it has a higher prediction accuracy.In addition,the fitting speed is improved.The feasibility of this network is verified through experiments.

关 键 词：光学器件 深度多层感知机 超宽带阶梯形微带单极子天线 Adam优化器 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]