![](/images/graphics-bg.png)
Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning
Joint Authors
Tian, Yubo
Gao, Jing
Zheng, Xie
Chen, Xuezhi
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-05-15
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
For the optimal design of electromagnetic devices, it is the most time consuming to obtain the training samples from full wave electromagnetic simulation software, including HFSS, CST, and IE3D.
Traditional machine learning methods usually use only labeled samples or unlabeled samples, but in practical problems, labeled samples and unlabeled samples coexist, and the acquisition cost of labeled samples is relatively high.
This paper proposes a semisupervised learning Gaussian Process (GP), which combines unlabeled samples to improve the accuracy of the GP model and reduce the number of labeled training samples required.
The proposed GP model consists two parts: initial training and self-training.
In the process of initial training, a small number of labeled samples obtained by full wave electromagnetic simulation are used for training the initial GP model.
Afterwards, the trained GP model is copied to another GP model in the process of self-training, and then the two GP models will update after crosstraining with different unlabeled samples.
Using the same test samples for testing and updating, a model with a smaller error will replace another.
Repeat the self-training process until a predefined stopping criterion is met.
Four different benchmark functions and resonant frequency modeling problems of three different microstrip antennas are used to evaluate the effectiveness of the GP model.
The results show that the proposed GP model has a good fitting effectiveness on benchmark functions.
For microstrip antennas resonant frequency modeling problems, in the case of using the same labeled samples, its predictive ability is better than that of the traditional supervised GP model.
American Psychological Association (APA)
Gao, Jing& Tian, Yubo& Zheng, Xie& Chen, Xuezhi. 2020. Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning. Complexity،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141457
Modern Language Association (MLA)
Gao, Jing…[et al.]. Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning. Complexity No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1141457
American Medical Association (AMA)
Gao, Jing& Tian, Yubo& Zheng, Xie& Chen, Xuezhi. Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning. Complexity. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1141457
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1141457