Wideband Monostatic RCS Prediction of Complex Objects using Support Vector Regression and Grey-wolf Optimizer

被引：0
|
作者：
Zhang, Zhourui [1 ]
Wang, Pengyuan [1 ]
He, Mang [1 ]
机构：
[1] Beijing Inst Technol, Sch Integrated Circuits & Elect, Beijing 100081, Peoples R China
来源：
APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL | 2023年 / 38卷 / 08期
关键词：
Complex objects; grey wolf optimizer; machine learning; radar cross-section; support vector regression; EFFICIENT;
D O I：
10.13052/2023.ACES.J.380808
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：
This paper presents a method based on the support vector regression (SVR) model and grey wolf optimizer (GWO) algorithm to efficiently predict the monostatic radar cross-section (mono-RCS) of complex objects over a wide angular range and frequency band. Using only a small-size of the mono-RCS data as the training set to construct the SVR model, the proposed method can predict accurate mono-RCS of complex objects under arbitrary incident angle over the entire by incorporating the meta-heuristic algorithm GWO. racy of the proposed SVR-GWO model over a wide frequency band.
引用
收藏
页码：609 / 615
页数：7
相关论文
共 50 条
  • [1] Hybrids of Support Vector Regression with Grey Wolf Optimizer and Firefly Algorithm for Spatial Prediction of Landslide Susceptibility
    Liu, Ru
    Peng, Jianbing
    Leng, Yanqiu
    Lee, Saro
    Panahi, Mahdi
    Chen, Wei
    Zhao, Xia
    [J]. REMOTE SENSING, 2021, 13 (24)
  • [2] A stock selection algorithm hybridizing grey wolf optimizer and support vector regression
    Liu, Meng
    Luo, Kaiping
    Zhang, Junhuan
    Chen, Shengli
    [J]. EXPERT SYSTEMS WITH APPLICATIONS, 2021, 179
  • [3] On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach
    Syah, Rahmad
    Naeem, Mohammad Hossein Towfighi
    Daneshfar, Reza
    Dehdar, Hossein
    Soulgani, Bahram Soltani
    [J]. PETROLEUM, 2022, 8 (02) : 264 - 269
  • [4] On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach
    Rahmad Syah
    Mohammad Hossein Towfighi Naeem
    Reza Daneshfar
    Hossein Dehdar
    Bahram Soltani Soulgani
    [J]. Petroleum, 2022, (02) : 264 - 269
  • [5] Prediction of coal spontaneous combustion temperature based on improved grey wolf optimizer algorithm and support vector regression
    Li, Shuang
    Xu, Kun
    Xue, Guangzhe
    Liu, Jiao
    Xu, Zhengquan
    [J]. FUEL, 2022, 324
  • [6] Remaining Useful Life Prediction for Fuel Cell Based on Support Vector Regression and Grey Wolf Optimizer Algorithm
    Chen, Kui
    Laghrouche, Salah
    Djerdir, Abdesslem
    [J]. IEEE TRANSACTIONS ON ENERGY CONVERSION, 2022, 37 (02) : 778 - 787
  • [7] Support Vector Regression Ship Motion Identification Modeling Based on Grey Wolf Optimizer
    Meng, Yao
    Zhang, Xiufeng
    Liu, Zhaochun
    Wang, Xiaoxue
    [J]. 2021 PROCEEDINGS OF THE 40TH CHINESE CONTROL CONFERENCE (CCC), 2021, : 1172 - 1176
  • [8] Optimizing support vector regression using grey wolf optimizer for enhancing energy efficiency and building prototype architecture
    Sakib, Mohd
    Ahmad, Shahnawaz
    Anwar, Khalid
    Saqib, Mohd
    [J]. Cluster Computing, 2025, 28 (01)
  • [9] Forecasting of Automobile Sales Based on Support Vector Regression Optimized by the Grey Wolf Optimizer Algorithm
    Qu, Fei
    Wang, Yi-Ting
    Hou, Wen-Hui
    Zhou, Xiao-Yu
    Wang, Xiao-Kang
    Li, Jun-Bo
    Wang, Jian-Qiang
    [J]. MATHEMATICS, 2022, 10 (13)
  • [10] Prediction of ash-deformation temperature based on grey-wolf algorithm and support-vector machine
    Xiao, Haiping
    Chen, Yuhui
    Dou, Chaozong
    Ru, Yu
    Cai, Li
    Zhang, Chun
    Kang, Zhizhong
    Sun, Baomin
    [J]. FUEL, 2019, 241 : 304 - 310
12345