Accurate microwave filter design based on particle swarm optimization and one‐dimensional convolution autoencoders

This paper proposes a one‐dimensional convolutional autoencoders (1D‐CAE) surrogate‐based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D‐CAE is a flexible neural network structure that can be used as surrogate mo...

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Vydáno v:	International journal of RF and microwave computer-aided engineering Ročník 32; číslo 4
Hlavní autoři:	Wang, Yanxing, Zhang, Zhuowei, Yi, Yaxin, Zhang, Yongliang
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Hoboken, USA John Wiley & Sons, Inc 01.04.2022
Témata:	Algorithms CAD Computer aided design Design optimization Filter design (mathematics) Inverse problems Mathematical models microwave filter design Microwave filters Modelling Neural networks one‐dimensional convolutional autoencoders Optimization techniques Parameters Particle swarm optimization surrogate model
ISSN:	1096-4290, 1099-047X
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Abstract	This paper proposes a one‐dimensional convolutional autoencoders (1D‐CAE) surrogate‐based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D‐CAE is a flexible neural network structure that can be used as surrogate model to handle forward modeling problems as well as solving inverse problems, such as extracting the coupling matrix from S‐parameters. In this paper, the 1D‐CAE is proposed to handle forward modeling problem. On the one hand, the 1D‐CAE model is adopted to as the surrogate model to the EM simulation solver to speed up the PSO algorithm optimization process. On the other hand, the 1D‐CAE model can update the data set and network parameters online to continuously improve the prediction accuracy of the surrogate model during the PSO algorithm iterations. Compared with other neural network structures, the 1D‐CAE model proposed in this paper requires less data and converges faster in the same test environment. To demonstrate the validity of the proposed method, it was used to design a sixth‐order and an eighth‐order cross‐coupled filter. The design results shown that the proposed method is valid.
AbstractList	This paper proposes a one‐dimensional convolutional autoencoders (1D‐CAE) surrogate‐based electromagnetic (EM) optimization technique exploiting particle swarm optimization (PSO) algorithm for microwave filter design. The 1D‐CAE is a flexible neural network structure that can be used as surrogate model to handle forward modeling problems as well as solving inverse problems, such as extracting the coupling matrix from S‐parameters. In this paper, the 1D‐CAE is proposed to handle forward modeling problem. On the one hand, the 1D‐CAE model is adopted to as the surrogate model to the EM simulation solver to speed up the PSO algorithm optimization process. On the other hand, the 1D‐CAE model can update the data set and network parameters online to continuously improve the prediction accuracy of the surrogate model during the PSO algorithm iterations. Compared with other neural network structures, the 1D‐CAE model proposed in this paper requires less data and converges faster in the same test environment. To demonstrate the validity of the proposed method, it was used to design a sixth‐order and an eighth‐order cross‐coupled filter. The design results shown that the proposed method is valid.
Author	Zhang, Zhuowei Yi, Yaxin Zhang, Yongliang Wang, Yanxing
Author_xml	– sequence: 1 givenname: Yanxing surname: Wang fullname: Wang, Yanxing organization: Inner Mongolia University – sequence: 2 givenname: Zhuowei surname: Zhang fullname: Zhang, Zhuowei organization: Nanjing Normal University – sequence: 3 givenname: Yaxin surname: Yi fullname: Yi, Yaxin organization: Inner Mongolia University – sequence: 4 givenname: Yongliang surname: Zhang fullname: Zhang, Yongliang email: namar@imu.edu.cn organization: Inner Mongolia University
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Cites_doi	10.1109/TMTT.2005.860327 10.1109/NEMO49486.2020.9343496 10.1137/S1052623496303470 10.1109/TMTT.2003.820897 10.1109/TMTT.2013.2296744 10.1109/TIE.2020.3009566 10.1002/mop.11187 10.1109/TMTT.2019.2952101 10.1109/6668.918262 10.1109/ICNN.1995.488968 10.1109/TAP.2018.2889029 10.1109/LMWC.2021.3062874 10.1109/TMAG.2005.846467 10.1109/ACCESS.2021.3063523 10.1109/TIE.2020.2987278 10.1017/S0962492900002518 10.1109/TMTT.2019.2932738 10.1109/MMM.2008.929554 10.1109/TEVC.2007.896686 10.1109/TMTT.2017.2661739 10.1109/APS.2000.873731 10.1002/adts.201800132
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Notes	Funding information This work was supported by the National Natural Science Foundation of China (NSFC) (Project Nos. 61761032 and 62161032) and Nature Science Foundation of Inner Mongolia (Contract No. 2019MS06006). ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
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SubjectTerms	Algorithms CAD Computer aided design Design optimization Filter design (mathematics) Inverse problems Mathematical models microwave filter design Microwave filters Modelling Neural networks one‐dimensional convolutional autoencoders Optimization techniques Parameters Particle swarm optimization surrogate model
Title	Accurate microwave filter design based on particle swarm optimization and one‐dimensional convolution autoencoders
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