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优化基于SSA-BP的田径训练方法及其对运动员爆炸力的影响

Ruibin Jing¹, Zhengwei Wang¹, Peng Suo²

¹School of Sports, DongShin University, Dongsindae-gil, Naju-si, Jeollanam-do 58245, South Korea.

|February 8, 2024

概括

这项研究使用子搜索和群算法增强了田径训练. 优化的训练显著提高了运动员的爆炸力和表现,将体育科学与计算机学科相结合.

科学领域:

体育科学与计算机科学整合
人工智能在运动训练中的应用

背景情况:

数字化和信息化是体育产业发展的关键趋势.
传统的培训方法有主观的限制.
在田径训练中需要科学理性.

研究的目的:

使用子搜索算法 (SSA) 优化神经网络概括.
通过群算法 (CSA) 改进运动员的训练计划 (组合,周期,强度).
增强田径训练方法的科学基础和有效性.

主要方法:

实现SSA以优化神经网络重量和值.
基于评估结果,利用CSA优化培训参数.
与其他优化算法相比,对SSA和CSA的性能分析.

主要成果:

SSA 显示出优异的参数匹配,溶液误差<0.50.0.
CSA显示出良好的准确性和速度,在优化方面表现优于其他模型.
弗里德曼测试证实了CSA对运动员的爆炸力和表现的显著积极影响.

结论:

开发的模型准确地评估了具有高可重复性的田径训练方法.
优化训练计划可以提高运动表现,并促进个性化训练.
这项研究促进了体育和计算机科学学科的整合.

关键词:

运动的爆炸性.在BP神经网络中,神经网络群群的优化群的优化新型智能优化算法智能优化算法优化算法优化算法斯帕罗搜索算法 (Sparrow search algorithm) 是一个非常简单的搜索算法.轨道运动和田野运动.

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