An Efficient Method for Antenna Design Based on a Self-Adaptive Bayesian Neural Network-Assisted Global Optimization Technique
Author:
Affiliation:
1. James Watt School of Engineering, University of Glasgow, Glasgow, U.K.
2. Faculty of Computing and Engineering, Wrexham Glyndwr University, Wrexham, U.K.
3. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, U.K.
Funder
MathWorks Development Collaboration Research Grant
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/8/9996294/09915328.pdf?arnumber=9915328
Reference49 articles.
1. Dual-Polarized Highly Folded Bowtie Antenna with Slotted Self-Grounded Structure for Sub-6 GHz 5G Applications
2. Efficient global optimisation of microwave antennas based on a parallel surrogate model‐assisted evolutionary algorithm
3. Low-Cost Modeling of Microwave Components by Means of Two-Stage Inverse/Forward Surrogates and Domain Confinement
4. Multistage Collaborative Machine Learning and its Application to Antenna Modeling and Optimization
5. An Efficient Method for Complex Antenna Design Based on a Self Adaptive Surrogate Model-Assisted Optimization Technique
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