基于卷积神经网络的微带滤波器结构参数估计

doi:10.13229/j.cnki.jdxbgxb20210440

Abstract

Abstract:

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.

Key words: electromagnetic, ring resonator, band pass filter, convolutional neural network, structure parameter estimation

CLC Number:

TN713

You-jun ZHANG,Shun-yan CHENG. Structure parameter estimation of microstrip filter based on convolutional neural network[J].Journal of Jilin University(Engineering and Technology Edition), 2022, 52(12): 3022-3028.

Figures/Tables 10

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References 18

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真实值			预测值			误差/%
L₃/mm	L₄/mm	S/mm	L₃/mm	L₄/mm	S/mm	误差/%
12.00	20.00	0.90	12.44	20.32	0.93	2.8
11.50	3.00	1.30	10.87	3.10	1.64	4.9
14.00	17.00	2.10	13.78	17.13	2.20	1.5

方法	均方误差	数据提取时间/h	训练时间/h	总耗时/h
文献［10］	6.59	13.00	0.25	13.25
文献［11］	142.80	-	6.40	6.40
本文	0.51	13.00	0.16	13.16

Structure parameter estimation of microstrip filter based on convolutional neural network

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