Structure parameter estimation of microstrip filter based on convolutional neural network

被引：0
|
作者：
Zhang Y.-J. [1 ]
Cheng S.-Y. [1 ]
机构：
[1] School of Information Engineering, Shanghai Maritime University, Shanghai
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2022年 / 52卷 / 12期
关键词：
band pass filter; convolutional neural network; electromagnetic; ring resonator; structure parameter estimation;
D O I：
10.13229/j.cnki.jdxbgxb20210440
中图分类号：
学科分类号：
摘要：
Aiming at the issue of microwave modeling based on neural network，a structure parameter estimation method for microstrip bandpass filter based on convolution neural network is proposed. A dual band，three band and four band microstrip filter was designed and analyzed by adopting single open stub loaded rectangular ring resonator and transverse signal interference technology. Applying electromagnetic simulation software extracted the model training data and the S-parameters and structure parameters of microstrip filter are designed as the input and output of the proposed convolution neural network，respectively. Furthermore，the trained model was used to predict the structure parameters of microstrip filter. The proposed method adopts convolutional neural network to design the microstrip filter，which can effectively solve the problems of many input parameters，complex model caused by full connection and long time-consuming when using neural network to design microstrip filter. The simulation results show that the S-parameters of the microstrip filter designed by this method have high accuracy. © 2022 Editorial Board of Jilin University. All rights reserved.
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页码：3022 / 3028
页数：6
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