Construction of beam quality factor prediction model based on support vector machine

被引:0
作者
Leng Kun [1 ]
Wu Wenyuan [1 ]
Zhang Xi [1 ]
Gong Yanchun [1 ]
Yang Yuntao [1 ]
机构
[1] PLA Army Engn Univ, Basic Dept, Nanjing, Jiangsu, Peoples R China
来源
FIFTH INTERNATIONAL SYMPOSIUM ON LASER INTERACTION WITH MATTER | 2019年 / 11046卷
关键词
Support vector machine; Beam quality factor; Multi-layer phase screen;
D O I
10.1117/12.2523900
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
The effectiveness of the laser can be evaluated by the beam qua lity factor, the beam quality factor is related to the initial parameters of the outgoing laser and various atmospheric parameters on the transmission path. Therefore, the prediction of the beam quality factor can be regarded as a statistical identification problem. Support vector machine has unique advantages in dealing with small sample, nonlinear and high dimensional problems, and can be used to deal with such statistical identification problems. This paper simulates the transmission process of Gaussian laser in the marine atmosphere based on the numerical simulation model of multi-layer phase screen, obtaining simulation data of initial laser radius, initial power, transmission distance, refractive index structure constant, atmospheric visibility, and spot radius, beam drift, and energy circle rate at the receiving surface; then constructing the prediction models of these beam quality factors by support vector machine, the optimal parameter model is obtained by adjusting the kernel function of the model, the insensitive loss coefficient and the penalty factor; finally, the prediction error and accuracy of the model are analyzed. The research results show that the support vector machine can fit the multiple regression relationship between input and output well, and the prediction accuracy of the model is high. The research results can provide a feasible basis for the application of support vector machine in the evaluation of the effectiveness of the laser in the marine atmosphere.
引用
收藏
页数:9
相关论文
共 50 条
  • [21] Spatio-temporal prediction and factor identification of urban air quality using support vector machine
    Liu, Chih-Chun
    Lin, Tzu-Chi
    Yuan, Kuang-Yu
    Chiueh, Pei-Te
    URBAN CLIMATE, 2022, 41
  • [22] Combined Prediction Model of Quantum Genetic Grey Prediction Model and Support Vector Machine
    Cao, Jiangyong
    Fang, Yilin
    Liu, Quan
    Liu, Aiming
    PROCEEDINGS OF 2016 5TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT), 2016, : 247 - 251
  • [23] A support vector machine model for the prediction of monomer reactivity ratios
    Xinliang Yu
    Xueye Wang
    Polymer Science Series B, 2011, 53
  • [24] Output Prediction Model in Fully Mechanized Mining Face Based on Support Vector Machine
    Li, Wanqing
    Meng, Wenqing
    Zhao, Yong
    Xu, Shipeng
    WKDD: 2009 SECOND INTERNATIONAL WORKSHOP ON KNOWLEDGE DISCOVERY AND DATA MINING, PROCEEDINGS, 2009, : 171 - +
  • [25] Disaster prediction model based on support vector machine for regression and improved differential evolution
    Xiaobing Yu
    Natural Hazards, 2017, 85 : 959 - 976
  • [26] A modified support vector machine based prediction model on streamflow at the Shihmen Reservoir, Taiwan
    Li, Pei-Hao
    Kwon, Hyun-Han
    Sun, Liqiang
    Lall, Upmanu
    Kao, Jehng-Jung
    INTERNATIONAL JOURNAL OF CLIMATOLOGY, 2010, 30 (08) : 1256 - 1268
  • [27] Risk Preference Based Support Vector Machine Inference Model for Slope Collapse Prediction
    Cheng, Min-Yuan
    Wu, Yu-Wei
    Chen, Kuan-Lin
    AUTOMATION IN CONSTRUCTION, 2012, 22 : 175 - 181
  • [28] GA-Based Support Vector Machine Model for the Prediction of Monthly Reservoir Storage
    Su, Jieqiong
    Wang, Xuan
    Liang, Yong
    Chen, Bin
    JOURNAL OF HYDROLOGIC ENGINEERING, 2014, 19 (07) : 1430 - 1437
  • [29] Terrorism Risk Prediction Model Based on Support Vector Machine Optimized by Whale Algorithm
    Luan, Meng
    Sun, Duoyong
    Li, Zhanfeng
    PROCEEDINGS OF 2019 IEEE 7TH INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND NETWORK TECHNOLOGY (ICCSNT 2019), 2019, : 166 - 169
  • [30] Disaster prediction model based on support vector machine for regression and improved differential evolution
    Yu, Xiaobing
    NATURAL HAZARDS, 2017, 85 (02) : 959 - 976