Surrogate Based Design Optimization of Multi-Band Antenna

被引：1
|
作者：
Tari, Ozlem [1 ]
Belen, Aysu [2 ]
Mahouti, Peyman [3 ]
Belen, Mehmet A. [2 ]
机构：
[1] Istanbul Arel Univ, Dept Math & Comp Sci, Istanbul, Turkey
[2] Iskenderun Tech Univ, Dept Hybrid & Elect Vehicles Technol, Antakya, Turkey
[3] Istanbul Cerrahpasa Univ, Dept Elect & Automat, Istanbul, Turkey
来源：
2021 INTERNATIONAL APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY SYMPOSIUM (ACES) | 2021年
关键词：
Artificial Neural Network; Multi-band antenna Optimization; Surrogate modeling; INVASIVE WEED OPTIMIZATION; YIELD ESTIMATION;
D O I：
10.1109/ACES53325.2021.00163
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this work, design optimization process of a multi-band antenna via the use of Artificial Neural Network (ANN) based surrogate model and meta-heuristic optimizers is studied. For this mean firstly, by using Latin-Hyper cube sampling method a data set based on 3D full wave EM simulator is generated to train an ANN based model. By using the ANN based surrogate model and a meta-heuristic optimizer Invasive Weed Optimization (IWO), design optimization of a multi-band antenna for (I) 2.4-3.6 GHz for ISM, LTE, and 5G sub frequencies, (II) 9-10 GHz for X band applications is aimed. Then the obtained results are compared with the simulated results of 3D EM simulation tool CST. Results show, that the proposed methodology provides a computationally efficient design optimization process for design optimization of multi-band antennas.
引用
收藏
页数：4
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