Tan, J., Shao, Y., Zhang, J. et al. (1 more author) (2021) Artificial neural network application in prediction of concrete embedded antenna performance. In: 2021 15th European Conference on Antennas and Propagation (EuCAP). 15th European Conference on Antennas and Propagation (EuCAP), 22-26 Mar 2021, Dusseldorf, Germany (virtual conference). Institute of Electrical and Electronics Engineers (IEEE) ISBN 9781728188454
Abstract
Artificial Neural Network (ANN) has been extensively applied to microwave device modeling, design and simulations. In the present paper, the prediction of concrete embedded antenna performance using ANN is presented. The ANN model takes antenna embedded depth and concrete dielectric constant as inputs and gives antenna radiation efficiency, gain and input impedance as outputs. The Particle Swarm Optimisation (PSO) is employed to search the global optimal weights and bias for ANN, then Bayesian Regularisation (BR) is used to train the ANN for overcoming the overfitting issue. It is found that the PSO computation iteration for optimal network weights and bias searching is less than gradient descent algorithm. A PSO-BR neural network (PSO-BRNN) and back-propagation neural network (BPNN) are trained to compute and predict the antenna performance. The PSO-BRNN performance is better than BPNN in terms of accuracy and generalisation.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021 The Authors. This is an author-produced version of a paper subsequently published in 15th European Conference on Antennas and Propagation (EuCAP) Proceedings. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Artificial neural network; Bayesian Regularisation; concrete embedded antenna; particle swarm optimisation |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Funding Information: | Funder Grant number European Commission - HORIZON 2020 752644; 843133 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Aug 2022 11:41 |
Last Modified: | 24 Aug 2022 07:20 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.23919/EuCAP51087.2021.9410952 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189977 |