2020
DOI: 10.1155/2020/3485469
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Resonant Frequency Modeling of Microwave Antennas Using Gaussian Process Based on Semisupervised Learning

Abstract: 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 unlabe… Show more

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Cited by 24 publications
(15 citation statements)
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“…Therefore, using a surrogate model instead of electromagnetic simulation software to evaluate the fitness of electromagnetic components can save optimization time, which is a popular topic of electromagnetic optimization design at present. Many modeling methods have been proposed by researchers, such as artificial neural network (ANN) [3][4][5], support vector machine (SVM) [6,7], extreme learning machine (ELM) [8][9][10], Gaussian process (GP) [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, using a surrogate model instead of electromagnetic simulation software to evaluate the fitness of electromagnetic components can save optimization time, which is a popular topic of electromagnetic optimization design at present. Many modeling methods have been proposed by researchers, such as artificial neural network (ANN) [3][4][5], support vector machine (SVM) [6,7], extreme learning machine (ELM) [8][9][10], Gaussian process (GP) [11][12][13].…”
Section: Introductionmentioning
confidence: 99%
“…In contrast, GP has a strict statistical theoretical basis, which is suitable for dealing with small samples, high dimensions, nonlinear and other complex problems [24]. The strong modeling capabilities of GP make it possible to adaptively obtain hyper-parameters and realize probabilistic prediction that is different from other regression models, so it is more and more widely used in the analysis of antenna modeling problems [25], [26], [27], [28], [29]. When electromagnetic performance is evaluated and calculated by electromagnetic simulation software, if HFSS or CST software is used, the simulation results not only include conventional electromagnetic performance, but also provide sensitivity information [30], [31], [32], [33].…”
Section: Introductionmentioning
confidence: 99%
“…erefore, many literatures have proposed that artificial neural networks (ANNs) [2], support vector machine (SVMs) [3], and Gaussian process (GP) [4,5] can be used to analyze antenna problems. ANN can implement parallel processing, selflearning, and nonlinear mapping, but its structure is relatively complicated, which requires a large amount of electromagnetic simulation data, and it is difficult to determine with poor generalization ability [6].…”
Section: Introductionmentioning
confidence: 99%