基于改进型LSTM神经网络的足球运动员体能训练节能健身研究

Nengchao Pan
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引用次数: 0

摘要

为了保证足球运动员的身体机能适应现代足球水平的发展,避免由于身体素质不足而无法适应现代足球比赛强度的现象。针对足球运动员的体能训练,提出了一种改进的长短期记忆网络(W-LSTM)模型,用于体能训练的优化和预测。该模型有效地结合了LSTM对时间序列数据的全局特征提取能力和提取数据的预处理能力,减少了特征信息的损失,实现了较高的预测精度。在LSTM的基础上增加前门,LSTM将训练和体能功能结合起来,减少数据异常值波动对预测结果的影响,有效提高体能训练优化预测的准确性,并以体型、运动耐量、运动强度和健身水平作为输入值,对W-LSTM、LM-BP和ARIMA三种模型进行对比实验。研究发现,W-LSTM具有较低的均方误差(0.011)和较高的相关系数(0.985),表明本文提出的模型在预测结果的准确性方面明显优于其他已有的比较模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on physical fitness training of football players based on improved LSTM neural network to improve physical energy saving and health
In order to ensure that the physical function of football players adapts to the development of modern football level, and avoid the phenomenon of inability to adapt to the intensity of modern football games due to lack of physical fitness. Aiming at the physical training of football players, this paper proposes an improved long-short-term memory network (W-LSTM) model for the optimization and prediction of physical training. The model effectively combines the global feature extraction ability of LSTM for time series data and the preprocessing ability of the extracted data, which reduces the loss of feature information and achieves high prediction accuracy. The front door is added on the basis of LSTM, which combines training and physical function to reduce the impact of fluctuations in data outliers on the prediction results, effectively improving the accuracy of physical training optimization and prediction, and using body shape, exercise tolerance, exercise intensity and fitness level as input values to conduct comparative experiments on the three models of W-LSTM, LM-BP and ARIMA. The study found that W-LSTM has a lower mean square error (0.011) and a higher correlation coefficient (0.985), indicating that the model proposed in this paper is significantly better than other existing comparison models in terms of the accuracy of prediction results.
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