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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Bibliographic Details
Published in:IEEE transactions on antennas and propagation Vol. 70; no. 7; pp. 5007 - 5014
Main Authors: Liu, Yan-Fang, Peng, Lin, Shao, Wei
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
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
Online Access:Get full text
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Summary: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.
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ISSN:0018-926X
1558-2221
DOI:10.1109/TAP.2022.3140313