A neural network model approach to athlete selection

被引：24
|
作者：
Maszczyk A. [1 ,2 ]
Zajac A. [1 ,2 ]
Ryguła I. [3 ]
机构：
[1] Chair of Sports Theory and Practice, Department of Methodology and Statistics, Academy of Physical Education, 40-065 Katowice
[2] 40-231 Katowice
[3] Department of System Analysis in Sport, Academy of Physical Education, 80-336 Gdańsk
来源：
Sports Engineering | 2011年 / 13卷 / 2期
关键词：
Artificial neural networks; Linear neural models; Multilayer perceptron (MLP); Non-linear neural models; Sports-selection; The method of Hellwig;
D O I：
10.1007/s12283-010-0055-y
中图分类号：
学科分类号：
摘要：
In order to determine the usefulness of neural models in optimisation of recruitment processes, statistical analyses were carried out on measured results of javelin throwers using a full take off. A group of 140 Polish junior javelin throwers took part in the research. In order to choose the optimum combination of model parameters the Hellwig method was used. Linear and multilayer perceptron neural models were constructed and used to calculate combinations of variables. Statistical analysis of the results showed that the linear model was not able to describe precisely the relationship between the dependent variable and independent variables for the investigated group of young javelin throwers. For the investigated group, the perceptron network with a 4-3-2-1 structure gave the best predictive relationship for sports results of the javelin throwers. © 2010 The Author(s).
引用
收藏
页码：83 / 93
页数：10
相关论文
共 50 条
  • [31] On the problem in model selection of neural network regression in overrealizable scenario
    Hagiwara, K
    [J]. NEURAL COMPUTATION, 2002, 14 (08) : 1979 - 2002
  • [32] Neural network model selection for financial time series prediction
    Virili, F
    Freisleben, B
    [J]. COMPUTATIONAL STATISTICS, 2001, 16 (03) : 451 - 463
  • [33] On the problem in model selection of neural network regression in overrealizable scenario
    Hagiwara, K
    Kuno, K
    Usui, S
    [J]. IJCNN 2000: PROCEEDINGS OF THE IEEE-INNS-ENNS INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOL VI, 2000, : 461 - 466
  • [34] Neural Network Model Selection for Financial Time Series Prediction
    Francesco Virili
    Bernd Freisleben
    [J]. Computational Statistics, 2001, 16 : 451 - 463
  • [35] Automating Deep Neural Network Model Selection for Edge Inference
    Lu, Bingqian
    Yang, Jianyi
    Chen, Lydia Y.
    Ren, Shaolei
    [J]. 2019 IEEE FIRST INTERNATIONAL CONFERENCE ON COGNITIVE MACHINE INTELLIGENCE (COGMI 2019), 2019, : 184 - 193
  • [36] Selection of Software Reliability Model Based on BP Neural Network
    Wu, Yingbo
    Wang, Xu
    [J]. ADVANCES IN SWARM INTELLIGENCE, PT I, 2011, 6728 : 489 - 496
  • [37] Intelligent access selection in cognitive networks: a fuzzy neural network approach
    Wang, Huiqiang
    Wang, Zhendong
    Feng, Guangsheng
    Lv, Hongwu
    Chen, Xiaoming
    Zhu, Qiang
    [J]. Journal of Computational Information Systems, 2012, 8 (21): : 8877 - 8884
  • [38] A neural network approach for selection of powder metallurgy materials and process parameters
    Cherian, RP
    Smith, LN
    Midha, PS
    [J]. ARTIFICIAL INTELLIGENCE IN ENGINEERING, 2000, 14 (01): : 39 - 44
  • [39] A hybrid convolutional neural network approach for feature selection and disease classification
    Department of Computer Science and Engineering, International Institute of Information Technology, Bhubaneswar, India
    [J]. Turk J Electr Eng Comput Sci, 1600, (2580-2599):
  • [40] A hybrid convolutional neural network approach for feature selection and disease classification
    Debata, Prajna Paramita
    Mohapatra, Puspanjali
    [J]. TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES, 2021, 29 : 2580 - 2599
12345