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

页数：4

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