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在体育教育中选择人工智能算法的一种混合型线性二进制模糊WASPAS方法
Yuanzhen Ni1, Fei Wang2, Hongzhen Zhang3
1Sports and Artificial Intelligence, School of Physical Education, Shandong University, Jinan, 250061, China. yuanzhen_ni@163.com.
Scientific reports
|September 1, 2025
概括
这项研究引入了一种混合决策模型,用于在体育教育中选择人工智能 (AI). 该模型有效地确定了基于卷积神经网络的运动分析 (CNN-MA) 作为PE应用的顶级AI算法.
科学领域:
- 教育技术
- 人工智能
- 决策科学
背景情况:
- 人工智能 (AI) 越来越多地融入体育教育 (PE),需要强大的决策 (DM) 方法来选择算法.
- 现有的方法往往与在PE中评估人工智能模型所固有的不确定性和模糊性作斗争.
研究的目的:
- 提出一种新的混合DM模型,将加权总和产品评估 (WASPAS) 技术与q-rung线性二氧化模糊集 (q-RLDFS) 框架结合起来.
- 解决在PE领域评估AI算法的结构化,耐不确定性方法的需求.
- 提高PE环境中的AI模型评估的可解释性和稳定性.
主要方法:
- 一个双层框架,集成WASPAS用于灵活的评分 (结合WSM和WPM) 和q-RLDFS用于高阶模糊建模和处理专家犹.
- 对五种人工智能算法 (CNN-MA,RL-TO,ES-EP,HAI-NLP,WSDMA) 的评估与八个关键PE相关标准相比.
- 敏感性和比较分析以验证模型的稳定性和可靠性.
主要成果:
- 基于卷积神经网络的运动分析 (CNN-MA) 被确定为PE中最有效的AI算法.
- 基于强化学习的训练优化器 (RL-TO) 被评为第二个最有效的解决方案.
- 通过严格的敏感性和比较分析,拟议的模型证明了其稳定性和可靠性.
结论:
- 开发的混合DM模型为PE采用人工智能提供了可扩展,透明和实用的决策支持工具.
- 为教育工作者,管理人员和决策者提供关于体育教育中战略性人工智能实施的可行建议.
- 极大地促进了PE领域内人工智能技术的明智选择和应用.
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