Application of Training Load Prediction Model based on Improved BP Neural Network in Sports Training of Athletes

Abstract: With the enhancement of data mining technology, competitive sports informatization has become an inevitable development trend. It has become a common phenomenon to use data mining technology to help athletes train scientifically, assist coaches in rational decision-making, and improve team competitiveness. In competitive sports, cyclists' adaptation to training has a complex relationship with their physical performance. In order to explore the correlation between data and provide better training data for athletes, this study proposes a load prediction model based on BP neural network (Back propagation, BP). Considering the local convergence and random assignment of traditional BP model, an adaptive genetic algorithm with improved selection operator is used to determine the initial weights and thresholds of BP neural network to improve the accuracy of the prediction model. The experimental results show that the improved adaptive genetic algorithm improves the overall optimization ability of the BP neural network, the improved BP neural network model has good stability in the convergence process, and the algorithm can search for better weight thresholds. Compared with the basic BP neural network prediction model, the accuracy of the optimized prediction model is increased by 11.86%, and the average error value is reduced by 26.21%, which is a guide to improve the training effect of the cycling team's competitive sports.

Lin Liu and Guannan Sheng. “Application of Training Load Prediction Model based on Improved BP Neural Network in Sports Training of Athletes”. International Journal of Advanced Computer Science and Applications (IJACSA) 13.10 (2022). http://dx.doi.org/10.14569/IJACSA.2022.0131076

@article{Liu2022,
title = {Application of Training Load Prediction Model based on Improved BP Neural Network in Sports Training of Athletes},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131076},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131076},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {10},
author = {Lin Liu and Guannan Sheng}
}