通过使用极端学习和累积优化算法来改进女子足球战术分析
Jun Li1, Mohammad Khishe2,3, Banar Fareed Ibrahim4
1School of Football, Chengdu Sport University, Chengdu, China.
Scientific reports
|November 29, 2025
概括
这项研究引入了一个新的深度学习框架,使用积累的黑猩猩优化算法 (ACHOA) 来分析女性.
科学领域:
- 体育科学与分析 运动科学与分析
- 运动中的人工智能
- 机器学习用于性能分析.
背景情况:
- 分析女性足球策略的传统方法,如深度卷积神经网络 (DCNN),是计算密集型的,不适合实时应用.
- 现有的深度极端学习机器 (DELM) 模型由于随机因素选择而遭受不可靠的预测,影响准确性.
- 需要先进的分析框架来准确评估女子足球中的球员表现和球队战术.
研究的目的:
- 引入一种新的深度学习框架来分析女子足球战略.
- 开发积累的黑猩猩优化算法 (ACHOA),以提高预测模型的可靠性和准确性.
- 为了实现女性足球的实时绩效监测和战术分析.
主要方法:
- 开发累积黑猩猩优化算法 (ACHOA) 以稳定深度极端学习机器 (DELM) 的参数.
- 实施ACHOA增强的DELM模型来分析玩家的有效性,技能估计和视频的位置错误.
- 训练和验证模型使用现实世界的视频数据从2021-2022年欧足联女子冠军联赛.
主要成果:
- 在ACHOA增强的DELM模型中,在女性足球视频数据上,分类准确度超过95%.
- 提出的方法证明了模型衍生因素和专家评估之间的更好的一致性.
- 模型的实时监控能力得到了验证,显示了战术调整的潜力.
结论:
- ACHOA算法显著提高了DELM模型用于体育分析的可靠性和性能.
- 开发的框架为女性足球中详细的球员和球队战略分析提供了强大的工具.
- 这项研究为改善女足战术决策,表现指标和比赛前战略开发提供了一条途径.
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