Dam Deformation Prediction Model Based on Combined Gaussian Process

被引:2
|
作者
Xiao, Bei [1 ]
Luo, Peng-Cheng [1 ]
Cheng, Zhi-Jun [1 ]
Zhang, Xiao-Nan [1 ]
Hu, Xin-Wu [1 ]
机构
[1] Natl Univ Def Technol, Dept Management Sci & Engn, Changsha, Peoples R China
关键词
Systematic combat effectiveness; Xgboost; Intelligent modeling technology Introduction;
D O I
10.1109/phm-qingdao46334.2019.8942944
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Systematic combat effectiveness evaluation is an important part of system combat research, which is of special significance for our army to adapt to modern war. The scale of systematic combat is large and the structure is complex, so it is difficult to complete the evaluation work with the traditional empirical method or mathematical method. In recent years, the simulation method which is widely used has achieved good results, but there are also problems such as huge computational cost. On the basis of system combat simulation, this paper uses xgboost to build an intelligent evaluation model of system combat effectiveness, which effectively solves the problem of traditional methods in calculating costs. Compared with the SVM method, the good performance of this method is proved.
引用
收藏
页数:6
相关论文
共 50 条
  • [21] Research on Energy Alternation Based on Gaussian Regression Combined Prediction Model
    Sun Haibin
    Lin Tao
    Zhang Shangang
    PROCEEDINGS OF 2019 2ND INTERNATIONAL CONFERENCE ON BIG DATA TECHNOLOGIES (ICBDT 2019), 2019, : 192 - 199
  • [22] MR and stacked GRUs neural network combined model and its application for deformation prediction of concrete dam
    Wen, Zhiping
    Zhou, Renlian
    Su, Huaizhi
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 201
  • [23] Deformation prediction model of large-span prestressed structure for health monitoring based on robust Gaussian process regression
    Fu, Wenwei
    Chen, Yi
    Luo, Yaozhi
    Wan, Hua-Ping
    Ma, Zhi
    Shen, Yanbin
    ENGINEERING STRUCTURES, 2024, 318
  • [24] Analysis of Concrete Dam Deformation Prediction Based on the ResNet-GRU-SGWO Model
    Ma, Ning
    Niu, Xiubo
    Chen, Xunhui
    Wei, Wenxiu
    Zhang, Ye
    Kang, Xinyu
    Zhong, Wen
    Wu, Jing
    ADVANCES IN CIVIL ENGINEERING, 2024, 2024
  • [25] Dam deformation prediction model based on self-adaptive temporal decomposition Screening
    Gu, Yu
    Su, Huaizhi
    Zhang, Shuai
    Yao, Kefu
    Liu, Mingkai
    Qi, Yining
    Shuili Xuebao/Journal of Hydraulic Engineering, 2024, 55 (09): : 1045 - 1057
  • [26] LSTM-Based Deformation Prediction Model of the Embankment Dam of the Danjiangkou Hydropower Station
    Wang, Shuming
    Yang, Bing
    Chen, Huimin
    Fang, Weihua
    Yu, Tiantang
    WATER, 2022, 14 (16)
  • [27] Automatic Concrete Dam Deformation Prediction Model Based on TPE-STL-LSTM
    Song, Sihan
    Zhou, Qiujing
    Zhang, Tao
    Hu, Yintao
    WATER, 2023, 15 (11)
  • [28] Dam Deformation Monitoring Model Based on Deep Learning and Split Conformal Quantile Prediction
    Su, Yan
    Fu, Jiayuan
    Lin, Weiwei
    Lin, Chuan
    Lai, Xiaohe
    Xie, Xiudong
    APPLIED SCIENCES-BASEL, 2025, 15 (04):
  • [29] Concrete dam deformation prediction model for health monitoring based on extreme learning machine
    Kang, Fei
    Liu, Jia
    Li, Junjie
    Li, Shouju
    STRUCTURAL CONTROL & HEALTH MONITORING, 2017, 24 (10):
  • [30] Dam Deformation Prediction Model Based on Multi-Scale Adaptive Kernel Ensemble
    Zhou, Bin
    Wang, Zixuan
    Fu, Shuyan
    Chen, Dehui
    Yin, Tao
    Gao, Lanlan
    Zhao, Dingzhu
    Ou, Bin
    WATER, 2024, 16 (13)