Ma, J. orcid.org/0000-0003-0583-4349, Dang, S. orcid.org/0000-0002-0018-815X, Watkins, G. orcid.org/0000-0001-9358-5041 et al. (2 more authors) (2023) A High-Performance Transfer Learning-Based Model for Microwave Structure Behavior Prediction. IEEE Transactions on Circuits and Systems II: Express Briefs, 70 (12). pp. 4394-4398. ISSN 1549-7747
Abstract
Microwave structure behavior prediction enables the estimation of circuit response over a frequency range, playing a crucial role in the design of radio frequency (RF) structures. Deep neural network (DNN) approaches have demonstrated their capability to simulate microwave structure behaviors. Nonetheless, the quality and utility of the model are constrained by the availability of data and computational capabilities. These inherent disadvantages hinder the extensive application of DNN in microwave structure behavior prediction. Transfer learning has recently been produced as a method offering improved accuracy and speed for predicting microwave circuit behavior. This paper proposes a novel transfer learning-based model to expedite the prediction process for a sequence of frequency samples. Through experimental validation, it is illustrated that the proposed methodology outperforms the conventional DNN techniques for microwave structure behavior prediction by effectively reducing the required data and shortening the training time. The proposed model also facilitates the fine-tuning of hyperparameters and reduces the simulator computing load.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Transfer learning, deep neural network, microwave behavior prediction, frequency response |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 09 Aug 2023 11:37 |
Last Modified: | 23 May 2024 15:15 |
Published Version: | https://ieeexplore.ieee.org/document/10185995 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Identification Number: | 10.1109/tcsii.2023.3296454 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:202276 |