A novel method for extracting and optimizing the complex permittivity of paper-based composites based on an artificial neural network model  

作　　者：XIA ChenBin SHEN JunYi LIAO ShaoWei WANG Yi HUANG ZhengSheng XUE Quan TANG Min LONG Jin HU Jian 

机构地区：[1]School of Light Industry and Engineering,South China University of Technology,Guangzhou 510641,China [2]School of Electronic and Information Engineering,South China University of Technology,Guangzhou 510641,China

出　　处：《Science China(Technological Sciences)》2024年第10期3190-3204,共15页中国科学（技术科学英文版）

基　　金：supported by the National Key Research and Development Program of China(Grant No.2021YFB3700104).

摘　　要：Measuring the complex permittivity of ultrathin,flexible materials with a high loss tangent poses a substantial challenge with precision using conventional methods,and verifying the accuracy of test results remains difficult.In this study,we introduce a methodology based on a back-propagation artificial neural network(ANN)to extract the complex permittivity of paper-based composites(PBCs).PBCs are ultrathin and flexible materials exhibiting considerable complex permittivity and dielectric loss tangent.Given the absence of mature measurement methods for PBCs and a lack of sufficient data for ANN training,a mapping relationship is initially established between the complex permittivity of honeycomb-structured microwave-absorbing materials(HMAMs,composed of PBCs)and that of PBCs using simulated data.Leveraging the ANN model,the complex permittivity of PBCs can be extracted from that of HMAMs obtained using standard measurement.Subsequently,two published methods are cited to illustrate the accuracy and advancement of the results obtained using the proposed approach.Additionally,specific error analysis is conducted,attributing discrepancies to the conductivity of PBCs,the homogenization of HMAMs,and differences between the simulation model and actual objects.Finally,the proposed method is applied to optimize the cell length parameters of HMAMs for enhanced absorption performance.The conclusion discusses further improvements and areas for extended research.

关 键 词：paper-based composite HONEYCOMB complex permittivity artificial neural networks inverse modeling 

分 类 号：TB332[一般工业技术—材料科学与工程] TP183[自动化与计算机技术—控制理论与控制工程]