Abstract
For the optimal design of electromagnetic components, surrogate model methods can usually be used, but obtaining labeled training samples from full-wave electromagnetic simulation software is most time-consuming. How to use relatively few labeled samples to obtain a relatively high-precision surrogate model is the current electromagnetic research hotspot. This article proposes a semi-supervised co-training algorithm based on Gaussian process (GP) and support vector machine (SVM). By using a small number of initial training samples, the initial GP model and initial SVM model can be trained by some basic parameter settings. Moreover, the accuracy of these two models can be improved by using the differences between these two models and combining with unlabeled samples for jointly training. In the co-training process, to ensure the performance of the proposed algorithm, a stop criterion set in advance to control the number of unlabeled samples introduced. Therefore, the accuracy of the model can be prevented from being reduced by introducing too much unlabeled samples, which can find the best solution in the limited time. The proposed co-training algorithm is evaluated by benchmark functions, optimal design of Yagi microstrip antenna (MSA) and GPS Beidou dual-mode MSA. The results show that the proposed algorithm fits the benchmark functions well. For the problem of resonant frequency modeling of the above two different MSAs, under the condition of using the same labeled samples, the predictive ability of the proposed algorithm is improved compared with the traditional supervised learning method. Moreover, for the groups of antenna sizes that meet the design requirements, the fitting effects of their return loss curve (S11) are well. The effectiveness of the proposed co-training algorithm has been well verified, which can be used to replace the time-consuming electromagnetic simulation software for prediction.
Highlights
In the optimization fields of electromagnetic components, it is common to use numerical simulation calculation or fullwave electromagnetic simulation software such as high frequency structure simulator (HFSS), computer simulation technology (CST) combined with global optimization algorithms [1]-[2]
In view of the above problems, this paper proposes a semi-supervised co-training algorithm based on Gaussian process (GP) model and support vector machine (SVM) model
In order to improve the optimization efficiency of electromagnetic components, reducing the number of times to call HFSS, and saving the time of obtaining labeled samples, this paper proposes a semi-supervised co-training algorithm based on GP model and SVM model
Summary
In the optimization fields of electromagnetic components, it is common to use numerical simulation calculation or fullwave electromagnetic simulation software such as high frequency structure simulator (HFSS), computer simulation technology (CST) combined with global optimization algorithms [1]-[2]. Electromagnetic problems are generally small sample ones, both GP and SVM modeling methods are widely used in antenna optimization. GP is a machine learning (ML) method gradually developed in recent years It has a strict statistical theoretical basis and is suitable for solving problems such as small samples, high dimensionality and nonlinearity. This study improves the traditional co-training method and proposes a co-training method based on GP and SVM, applying SSL algorithm to the field of electromagnetic optimization. For the experiments of two different antennas optimization, the proposed co-training algorithm has better predictive ability than that of traditional supervised learning method by using the same label samples
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