RESEARCH ON ATHLETES' PSYCHOLOGICAL REGULATION ABILITY BASED ON BACK PROPAGATION (BP) NEURAL NETWORK ALGORITHM

被引:0
|
作者
Tan, Yun [1 ]
Zhang, Guoqing [2 ]
机构
[1] Southwest Univ, Phys Educ Sch, Publ Hlth Educ, Chongqing 400715, Peoples R China
[2] Chong Qing Elect Power Coll, Publ Sports, Chongqing 400053, Peoples R China
关键词
Neural Networks; Computer; Athletes; Handling; Psychological; PERFORMANCE;
D O I
10.1590/1517-8692202127022021_0041
中图分类号
Q4 [生理学];
学科分类号
071003 ;
摘要
Athletes' psychological control ability directly affects competitions. Therefore, it is necessary to supervise the athletes' game psychology. Athletes' game state supervision model is constructed through the facial information extraction algorithm. The homography matrix and the calculation method are introduced. Then, two methods are introduced to solve the rotation matrix from the homography matrix. After the rotation matrix is solved, the method of obtaining the facial rotation angle from the rotation matrix is introduced. The two methods are compared in the simulation data, and the advantages and disadvantages of each algorithm are analyzed to determine the method used in this paper. The experimental results show that the model prediction accuracy reaches 70%, which can effectively supervise the psychological state of athletes. This research study is of great significance to improve the performance of athletes in competitions and improve the application of back propagation (BP) neural network algorithm.
引用
收藏
页码:83 / 86
页数:4
相关论文
共 50 条
  • [11] Algorithm Study on Physical Adaptive Regulation Based on BP Neural Network
    Yuan, Zhiliang
    [J]. APPLIED SCIENCE, MATERIALS SCIENCE AND INFORMATION TECHNOLOGIES IN INDUSTRY, 2014, 513-517 : 2364 - 2368
  • [12] Study on the Improved Algorithm of Algorithm Back Propagation Neural Network
    Cao, Xiao-ping
    Luo, Xian-wen
    [J]. 2015 INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND TECHNOLOGY (ICCST 2015), 2015, : 298 - 303
  • [13] Research on a back-propagation neural network based Q learning algorithm in multi agent system
    Lin, OY
    Guo, QP
    Santai, OY
    [J]. DCABES 2004, Proceedings, Vols, 1 and 2, 2004, : 784 - 789
  • [14] Prediction of cut size for pneumatic classification based on a back propagation (BP) neural network
    Wu, Shubo
    Liu, Jiaxiang
    Yu, Yuan
    [J]. ZKG INTERNATIONAL, 2016, 69 (11): : 64 - 71
  • [15] Research and Optimization of BP Neural Network Algorithm
    Wang Xian-ping
    [J]. 2015 SEVENTH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION (ICMTMA 2015), 2015, : 818 - 822
  • [16] Research and Application on BP Neural Network Algorithm
    Yan, Zhao
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL INDUSTRIAL INFORMATICS AND COMPUTER ENGINEERING CONFERENCE, 2015, : 1444 - 1447
  • [17] Fault Location of Distribution Network Based on Back Propagation Neural Network Optimization Algorithm
    Zhou, Chuan
    Gui, Suying
    Liu, Yan
    Ma, Junpeng
    Wang, Hao
    [J]. PROCESSES, 2023, 11 (07)
  • [18] A Research on Evaluation of Regional Technological Innovation Ability Based on BP Neural Network
    Yan-ping, Yang
    [J]. 2007 INTERNATIONAL CONFERENCE ON WIRELESS COMMUNICATIONS, NETWORKING AND MOBILE COMPUTING, VOLS 1-15, 2007, : 5523 - 5526
  • [19] Programming the Algorithm Learning Neural Network Back Propagation
    Sergey, Stepanuk
    [J]. MEMSTECH: 2009 INTERNATIONAL CONFERENCE ON PERSPECTIVE TECHNOLOGIES AND METHODS IN MEMS DESIGN, 2009, : 65 - 66
  • [20] Back propagation algorithm of neural network with global optimization
    Lu, Baiqian
    Li, Tianduo
    [J]. Qinghua Daxue Xuebao/Journal of Tsinghua University, 1997, 37 (02): : 32 - 34