Athletic Performance Prediction Model Design Based on Grey BP Neural Network
- DOI
- 10.2991/icmmcce-15.2015.303How to use a DOI?
- Keywords
- Athletic Performance; Women’s Heptathlon; Grey BP Neural Network Prediction Model
- Abstract
The best annual performances of the world women’s pentathlons during 2005~2013 are statistically collected in this article, and the prediction of the best performance of the world women’s heptathlon in 2013 is taken as the research object. According to the best annual performances of the world women’s heptathlons during 2005~2012, the athletic performance prediction model composed of GM(1,1) grey prediction model and BP neural network prediction model in serial connection is established in this article, and this model is applied to predict the best annual performance of the world women’s heptathlon in 2013. Through the comparison of the actual value of the best annual performance of the world women’s heptathlon in 2013 and the predicted value of the model, the application of the grey BP neural network prediction model in athletic performance prediction is researched and analyzed in this article. The research result shows that for the athletic performance prediction problem, the grey BP neural network prediction model has the features of high prediction accuracy, simple application and strong generalization performance, and this model is also superior to single GM(1,1) grey prediction model and BP neural network model.
- Copyright
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Ling Jia AU - Qing-bin Wang PY - 2015/12 DA - 2015/12 TI - Athletic Performance Prediction Model Design Based on Grey BP Neural Network BT - Proceedings of the 4th International Conference on Mechatronics, Materials, Chemistry and Computer Engineering 2015 PB - Atlantis Press SN - 2352-538X UR - https://doi.org/10.2991/icmmcce-15.2015.303 DO - 10.2991/icmmcce-15.2015.303 ID - Jia2015/12 ER -