Assessing the effectiveness based on principal component analysis and support vector machine

被引：0
|
作者：
机构：
来源：
Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron | 2006年 / 6卷 / 889-891+940期
关键词：
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Several methods for assessing the effectiveness of weapon system are discussed, and their characteristics are analyzed. Establishing the parameters-effectiveness mode of weapon system, the first place is to select the character parameters of weapon system. The character parameters of weapon system are selected based on principal component analysis. A parameters-effectiveness model is established by using support vector machine. The method is illustrated through examples and is compared with the neural network method. The comparing results show that the support vector machine method is more accurate and simple.
引用
收藏
相关论文
共 50 条
  • [21] Separating volcanic rock groups: a novel method based on principal component analysis and a support vector machine
    Yu Q.
    Zhang X.
    Hu B.
    Zhang D.
    Arabian Journal of Geosciences, 2021, 14 (11)
  • [22] Digital watermark extraction using support vector machine with principal component analysis based feature reduction
    Verma, Vivek Singh
    Jha, Rajib Kumar
    Ojha, Aparajita
    JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION, 2015, 31 : 75 - 85
  • [23] The Development of a Fault Diagnosis Model Based on Principal Component Analysis and Support Vector Machine for a Polystyrene Reactor
    Jeong, Yeonsu
    Lee, Chang Jun
    KOREAN CHEMICAL ENGINEERING RESEARCH, 2022, 60 (02): : 223 - 228
  • [24] A direct method of nuclear pulse shape discrimination based on principal component analysis and support vector machine
    Zhang, Z. H.
    Hu, C. Y.
    Fan, X. Y.
    Liao, B.
    Li, Y. T.
    Zhu, J. J.
    Zhang, L. Q.
    JOURNAL OF INSTRUMENTATION, 2019, 14
  • [25] Discrimination of Varieties of Borneo! Using Terahertz Spectra Based on Principal Component Analysis and Support Vector Machine
    Li Wu
    Hu Bing
    Wang Ming-wei
    SPECTROSCOPY AND SPECTRAL ANALYSIS, 2014, 34 (12) : 3235 - 3240
  • [26] Series Arc Detection and Complex Load Recognition Based on Principal Component Analysis and Support Vector Machine
    Jiang, Jun
    Wen, Zhe
    Zhao, Mingxin
    Bie, Yifan
    Li, Chen
    Tan, Mingang
    Zhang, Chaohai
    IEEE ACCESS, 2019, 7 : 47221 - 47229
  • [27] Classification of Foreign Language Mobile Learning Strategy Based on Principal Component Analysis and Support Vector Machine
    Hu, Shuai
    Gu, Yan
    Cheng, Yingxin
    INFORMATION TECHNOLOGY AND INTELLIGENT TRANSPORTATION SYSTEMS, VOL 2, 2017, 455 : 371 - 380
  • [28] Tool wear condition monitoring based on principal component analysis and C-support vector machine
    Xie N.
    Ma F.
    Duan M.
    Li A.
    Tongji Daxue Xuebao/Journal of Tongji University, 2016, 44 (03): : 434 - 439
  • [29] Tool wear prediction based on kernel principal component analysis and least square support vector machine
    Gao, Kangping
    Xu, Xinxin
    Jiao, Shengjie
    MEASUREMENT SCIENCE AND TECHNOLOGY, 2024, 35 (10)
  • [30] Seismic landslide susceptibility assessment using principal component analysis and support vector machine
    Xu, Ziyao
    Che, Ailan
    Zhou, Hanxu
    SCIENTIFIC REPORTS, 2024, 14 (01)
12345