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Liu, Y, Liu, B, Ur-Rehman, M, Imran, M, Akinsolu, MO, Excell, P and Hua, Q (2022) An Efficient Method for Antenna Design Based on a Self-Adaptive Bayesian Neural Network Assisted Global Optimization Technique. IEEE Transactions on Antennas and Propagation, 70 (12). pp. 11375-11388. ISSN 0018-926X
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An Efficient Method for Antenna Design Based on a Self-Adaptive Bayesian Neural Network Assisted Global Optimization Technique.pdf - Accepted Version Download (797kB) | Preview |
Abstract
Gaussian process (GP) is a very popular machine learning method for online surrogate model-assisted antenna design optimization. Despite many successes, two improvements are important for GP-based antenna global optimization methods, including (1) the convergence speed (i.e., the number of necessary electromagnetic simulations to obtain a high-performance design), and (2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called self-adaptive Bayesian neural network surrogate model-assisted differential evolution for antenna optimization (SB-SADEA), is presented in this paper. The key innovations include: (1) The introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and (2) a bespoke self-adaptive lower confidence bound method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared to state-of-the-art GP-based antenna global optimization methods.
| Item Type: | Article |
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| Additional Information: | © 2022 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. |
| Uncontrolled Keywords: | 0906 Electrical and Electronic Engineering; 1005 Communications Technologies; Networking & Telecommunications |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TK Electrical engineering. Electronics. Nuclear engineering |
| Divisions: | Civil Engineering & Built Environment |
| Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 11 Jan 2023 10:51 |
| Last Modified: | 11 Jan 2023 11:00 |
| DOI or ID number: | 10.1109/TAP.2022.3211732 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18582 |
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