College of Sports and Physical Education, Shandong Sport University, Rizhao 276827, Shandong, China.
Sports Section, Guangshui Road Primary School, Qingdao 266100, Shandong, China.
Comput Intell Neurosci. 2022 Mar 7;2022:5034081. doi: 10.1155/2022/5034081. eCollection 2022.
This paper examines the problem of athletes' training in sports, exploring the methods and means by which athletes can perform difficult movements in which they normally make minor training errors in order to achieve better competition results and placements. To this end, we test the explanatory and predictive effects of a theoretical model starting with planned behaviour and then use exercise planning, self-efficacy, and support as variables to develop a partial least squares regression model of sports to improve the explanation and prediction of sporting athletes' intentions and behaviour. An improved RBF network-based method for player behaviour prediction is proposed. On the basis of the RBF analysis, the number of layers and the number of neurons in the hidden layer of the network are adjusted and optimised, respectively, to improve its generalisation and learning abilities, and the athlete behaviour prediction model is given. The results demonstrate the advantages of the improved algorithm, which in turn provides a more scientific approach to the current basketball training.
本文研究了运动员在体育运动中的训练问题，探讨了运动员在正常训练中出现小错误的情况下完成高难度动作的方法和手段，以期取得更好的比赛成绩和名次。为此，我们从计划行为理论入手，检验了一个理论模型的解释和预测效果，然后使用运动规划、自我效能和支持作为变量，开发了一个体育的偏最小二乘回归模型，以提高对运动运动员意图和行为的解释和预测。提出了一种基于改进的 RBF 网络的球员行为预测方法。在 RBF 分析的基础上，分别调整和优化网络的层数和隐藏层神经元数，以提高其泛化和学习能力，给出了运动员行为预测模型。结果表明改进算法的优势，为当前篮球训练提供了更科学的方法。
