Shape Modeling of Microstrip Filters Based on Convolutional Neural Network

被引：9

作者：

Luo, Hai-Ying ^{[1
]}

Shao, Wei ^{[1
]}

Ding, Xiao ^{[1
]}

Wang, Bing-Zhong ^{[1
]}

Cheng, Xi ^{[2
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Phys, Chengdu 611731, Peoples R China

[2] Xinjiang Agr Univ, Sch Comp & Informat Engn, Urumqi 830052, Xinjiang, Peoples R China

来源：

IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS | 2022年 / 32卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Shape; Training; Convolutional neural networks; Strips; Interpolation; Splines (mathematics); Microwave imaging; Convolutional neural network (CNN); cubic spline interpolation; microstrip filters; shape modeling; DESIGN;

D O I：

10.1109/LMWC.2022.3162414

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

An effective convolutional neural network (CNN) with the transfer function (TF) is proposed for shape modeling of electromagnetic (EM) behaviors of microstrip filters. The input of CNN is the images of metallic strips instead of the geometric parameters. To define the training samples, a one-to-one relation between the strip contour and the knot positions is built with a shape-changing technique based on cubic spline interpolation. The proposed model is confirmed with an example of a microstrip/coplanar waveguide (CPW) ultrawideband (UWB) filter. Compared with the parametric artificial neural network (ANN) and the shape ANN, the proposed model shows the improvement of design flexibility and the expansion of the solution domain.

引用

页码：1019 / 1022

页数：4

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