Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Ultra-Wideband (UWB) characteristic estimation of elliptic patch antenna based on machine learning techniques
Frequenz ( IF 1.1 ) Pub Date : 2020-09-25 , DOI: 10.1515/freq-2019-0210 Duygu Nazan Gençoğlan 1 , Mustafa Turan Arslan 2 , Şule Çolak 1 , Esen Yildirim 1
Frequenz ( IF 1.1 ) Pub Date : 2020-09-25 , DOI: 10.1515/freq-2019-0210 Duygu Nazan Gençoğlan 1 , Mustafa Turan Arslan 2 , Şule Çolak 1 , Esen Yildirim 1
Affiliation
Abstract In this study, estimation of Ultra-Wideband (UWB) characteristics of microstrip elliptic patch antenna is investigated by means of k-nearest neighborhood algorithm. A total of 16,940 antennas are simulated by changing antenna dimensions and substrate material. Antennas are examined by observing Return Loss and Voltage Standing Wave Ratio (VSWR) characteristics. In the study, classification of antennas in terms of having UWB characteristics results in accuracies higher than 97%. Additionally, Consistency based Feature Selection method is applied to eliminate redundant and irrelevant features. This method yields that substrate material does not affect the UWB characteristics of the antenna. Classification process is repeated for the reduced feature set, reaching to 97.44% accuracy rate. This result is validated by 854 antennas, which are not included in the original antenna set. Antennas are designed for seven different substrate materials keeping all other parameters constant. Computer Simulation Technology Microwave Studio (CST MWS) is used for the design and simulation of the antennas.
中文翻译:
基于机器学习技术的椭圆贴片天线超宽带(UWB)特性估计
摘要 本研究采用k-近邻算法对微带椭圆贴片天线的超宽带(UWB)特性进行了研究。通过改变天线尺寸和基板材料,总共模拟了 16,940 根天线。通过观察回波损耗和电压驻波比 (VSWR) 特性来检查天线。在研究中,根据具有 UWB 特性对天线进行分类,其准确率高于 97%。此外,还应用了基于一致性的特征选择方法来消除冗余和不相关的特征。这种方法产生的基板材料不会影响天线的 UWB 特性。对减少的特征集重复分类过程,准确率达到97.44%。这个结果得到了 854 根天线的验证,不包含在原始天线组中。天线设计用于七种不同的基板材料,保持所有其他参数不变。计算机仿真技术 Microwave Studio (CST MWS) 用于天线的设计和仿真。
更新日期:2020-09-25
中文翻译:
基于机器学习技术的椭圆贴片天线超宽带(UWB)特性估计
摘要 本研究采用k-近邻算法对微带椭圆贴片天线的超宽带(UWB)特性进行了研究。通过改变天线尺寸和基板材料,总共模拟了 16,940 根天线。通过观察回波损耗和电压驻波比 (VSWR) 特性来检查天线。在研究中,根据具有 UWB 特性对天线进行分类,其准确率高于 97%。此外,还应用了基于一致性的特征选择方法来消除冗余和不相关的特征。这种方法产生的基板材料不会影响天线的 UWB 特性。对减少的特征集重复分类过程,准确率达到97.44%。这个结果得到了 854 根天线的验证,不包含在原始天线组中。天线设计用于七种不同的基板材料,保持所有其他参数不变。计算机仿真技术 Microwave Studio (CST MWS) 用于天线的设计和仿真。