Issue |
MATEC Web Conf.
Volume 395, 2024
2023 2nd International Conference on Physics, Computing and Mathematical (ICPCM2023)
|
|
---|---|---|
Article Number | 01012 | |
Number of page(s) | 7 | |
DOI | https://doi.org/10.1051/matecconf/202439501012 | |
Published online | 15 May 2024 |
WLAN monopole antenna design by Siamese convolutional neural network and KNN exploiting Gaussian process
School of Information and Communication Engineering, Guangzhou Maritime University, Guangzhou, China
* Corresponding author: tianyubo@gzmtu.edu.cn
In the process of antenna design, surrogate models can generally be used, but modeling requires a large number of samples. Although full wave electromagnetic simulation software can handle this task, obtaining a large number of samples is time-consuming, however too small number of sample may lead to lower accuracy of the trained surrogate model. Inspired by semi-supervised learning methods, this paper uses Siamese convolutional neural networks (SCNN) and K-nearest neighbor (KNN) algorithms to generate highly reliable virtual samples and expand the training sample set, further improving the accuracy and robustness of the surrogate model by exploiting Gaussian process (GP) models. The proposed method is named SCNN-KNN-GP, which is used for the design of WLAN dual band monopole antennas. Moreover, the relationships between the performance of the proposed model and the increased number of virtual samples and the coefficient of the KNN are studied, resulting in a more excellent surrogate model structure.
Key words: Siamese neural networks / K-nearest neighbour / Gaussian processes / Monopole antennas / Modelling / Optimization
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.