基于机器学习算法的6.0 GHz以下介质谐振器MIMO天线性能预测

IF 0.6 4区工程技术 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC

Electromagnetics Pub Date : 2023-11-19 DOI:10.1080/02726343.2023.2281480

Khushboo Pachori, Amit Prakash, Nagendra Kumar

引用次数: 0

摘要

在本文中，使用各种机器学习算法，即深度神经网络(DNN)，随机森林和XG boost，对双端口介质谐振器天线进行建模。的独特性质……

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Performance prediction of dielectric resonator based MIMO antenna for sub-6.0 GHz using machine learning algorithms

In this article, a dual port dielectric resonator antenna is modeled using various machine learning algorithms i.e. deep neural network (DNN), Random Forest, and XG boost. The unique properties of ...

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来源期刊

Electromagnetics 工程技术-工程：电子与电气

CiteScore

1.60

自引率

12.50%

发文量

审稿时长

6 months

期刊介绍： Publishing eight times per year, Electromagnetics offers refereed papers that span the entire broad field of electromagnetics and serves as an exceptional reference source of permanent archival value. Included in this wide ranging scope of materials are developments in electromagnetic theory, high frequency techniques, antennas and randomes, arrays, numerical techniques, scattering and diffraction, materials, and printed circuits. The journal also serves as a forum for deliberations on innovations in the field. Additionally, special issues give more in-depth coverage to topics of immediate importance. All submitted manuscripts are subject to initial appraisal by the Editor, and, if found suitable for further consideration, to peer review by independent, anonymous expert referees. Submissions can be made via email or postal mail.