An Efficient Knowledge-Based Artificial Neural Network for the Design of Circularly Polarized 3-D-Printed Lens Antenna

An efficient knowledge-based artificial neural network (KBANN) is proposed, and it is used for the design of circularly polarized (CP) lens antenna in this article. In this KBANN, forward neural network (FNN) and inverse neural network (INN) are included. In this model, INN is the major component to...

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Vydané v:	IEEE transactions on antennas and propagation Ročník 70; číslo 7; s. 5007 - 5014
Hlavní autori:	Liu, Yan-Fang, Peng, Lin, Shao, Wei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 01.07.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	3-D-printed Antenna design Antennas Artificial neural networks Circular polarization circularly polarized~(CP) antenna forward neural network (FNN) High gain inverse neural network (INN) Lens antennas Lenses Mathematical models Microstrip antennas Neurons Parameters Performance indices Predictive models prior knowledge Three dimensional printing Training
ISSN:	0018-926X, 1558-2221
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Abstract	An efficient knowledge-based artificial neural network (KBANN) is proposed, and it is used for the design of circularly polarized (CP) lens antenna in this article. In this KBANN, forward neural network (FNN) and inverse neural network (INN) are included. In this model, INN is the major component to predict the antenna structure parameters. As multiple performance indices are required, INN requires a large number of training samples to deduce complex mapping relationship. To solve this problem, FNN is introduced to provide prior knowledge for INN. FNN generates a huge training dataset for INN training, and then the trained INN can directly output the geometric parameters by feeding the target electromagnetic responses as input. This article solves the problem of multiple performance indices in antenna design, and a CP lens antenna with wideband, good axial ratio, and high gain is designed and fabricated to verify the effectiveness of the KBANN model.
AbstractList	An efficient knowledge-based artificial neural network (KBANN) is proposed, and it is used for the design of circularly polarized (CP) lens antenna in this article. In this KBANN, forward neural network (FNN) and inverse neural network (INN) are included. In this model, INN is the major component to predict the antenna structure parameters. As multiple performance indices are required, INN requires a large number of training samples to deduce complex mapping relationship. To solve this problem, FNN is introduced to provide prior knowledge for INN. FNN generates a huge training dataset for INN training, and then the trained INN can directly output the geometric parameters by feeding the target electromagnetic responses as input. This article solves the problem of multiple performance indices in antenna design, and a CP lens antenna with wideband, good axial ratio, and high gain is designed and fabricated to verify the effectiveness of the KBANN model.
Author	Shao, Wei Liu, Yan-Fang Peng, Lin
Author_xml	– sequence: 1 givenname: Yan-Fang surname: Liu fullname: Liu, Yan-Fang organization: Key laboratory of Microwave and Optical Wave Application Technology, Guilin University of Electronic Technology, Guilin, Guangxi, China – sequence: 2 givenname: Lin orcidid: 0000-0002-1255-3118 surname: Peng fullname: Peng, Lin email: penglin528@hotmail.com organization: Key laboratory of Microwave and Optical Wave Application Technology, Guilin University of Electronic Technology, Guilin, Guangxi, China – sequence: 3 givenname: Wei orcidid: 0000-0001-9515-7091 surname: Shao fullname: Shao, Wei email: weishao@uestc.edu.cn organization: School of Physics, University of Electronic Science and Technology of China, Chengdu, China
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SubjectTerms	3-D-printed Antenna design Antennas Artificial neural networks Circular polarization circularly polarized~(CP) antenna forward neural network (FNN) High gain inverse neural network (INN) Lens antennas Lenses Mathematical models Microstrip antennas Neurons Parameters Performance indices Predictive models prior knowledge Three dimensional printing Training
Title	An Efficient Knowledge-Based Artificial Neural Network for the Design of Circularly Polarized 3-D-Printed Lens Antenna
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