Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

Authors

  • Tianliang Zhang School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China
  • Yubo Tian School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China
  • Xuezhi Chen School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China
  • Jing Gao School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China

Keywords:

AdaBoost algorithm, Gaussian process ensemble, microstrip antenna, resonant frequency

Abstract

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on AdaBoost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the AdaBoost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.

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Author Biographies

Tianliang Zhang, School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China

Tianliang Zhang was born in Maanshan, China, 1994. In 2018, he received his bachelor's degree in Electrical and Electronics Engineering from West Anhui University, China. Now, he is a master candidate in Jiangsu University of Science and Technology. His current research interests include the design of microstrip antenna and optimization, machine learning and optimization algorithm.

Yubo Tian, School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China

Yubo Tian was born in Changtu, China, in 1971. He received the Ph.D. degree at Nanjing University in 2004. He is a Full Professor with the School of Electronics and Information, Jiangsu University of Science and Technology now. His research interest is applications of computational intelligence to the electromagnetics field.

Xuezhi Chen, School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China

Xuezhi Chen was born in Nantong, China, in 1995. He received the Tech. bachelor degree in Nanjing Institute of Technology. Now, he is studying for master’s degree in Jiangsu University of Science and Technology. His research interest is rapid optimization design of microwave devices.

Jing Gao, School of Electronics and Information Jiangsu University of Science and Technology, Zhenjiang 212003, China

Jing Gao was born in Huaian, Jiangsu Province, China, in 1995. She is a master student at Jiangsu University of Science and Technology now. Her research interests include signal processing theory and technology

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Published

2020-12-05

How to Cite

[1]
Tianliang Zhang, Yubo Tian, Xuezhi Chen, and Jing Gao, “Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble”, ACES Journal, vol. 35, no. 12, pp. 1485–1492, Dec. 2020.

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