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Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

Authors :
Tianliang Zhang
Yubo Tian
Xuezhi Chen
Jing Gao
Source :
Applied Computational Electromagnetics Society. 35:1485-1492
Publication Year :
2021
Publisher :
River Publishers, 2021.

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.

Details

ISSN :
10544887
Volume :
35
Database :
OpenAIRE
Journal :
Applied Computational Electromagnetics Society
Accession number :
edsair.doi...........9bcbb652d43e9e4b5d3fb0b29ca9f8ef
Full Text :
https://doi.org/10.47037/2020.aces.j.351205