Chongqing Jiaotong University, Chongqing 400074, China.
School of Sciences, Zhejiang SCI-TECH University, Hangzhou, Zhejiang 310000, China.
Comput Intell Neurosci. 2022 Aug 30;2022:6431776. doi: 10.1155/2022/6431776. eCollection 2022.
By the method of documentation and logical analysis, based on the data, based on logic and based on the knowledge of three kinds of artificial intelligence in the sports education, the intelligent learning system feedback delay are studied, combined with mobile communication which led to the artificial intelligence online sports games teaching, pattern recognition, and virtual technology combined with innovative teaching interaction and experience. Promoting the development of green PE teaching machine learning can identify the types of PE activities and realize efficient PE learning diagnosis. Intelligent decision support system can identify sports talents and improve the effect of personalized PE teaching evaluation. From the perspective of psychological development and education, the key problems to be solved in the integration of artificial intelligence and physical education are examined. Then, the consistent model predictive control for feedback delay of nonlinear sports learning multiagent system with network induced delay and random communication protocol is studied. Under the communication waiting mechanism designed, each agent has a certain tolerance of delay, and this tolerance can be determined by ensuring the stability of the system. At the same time, a random communication protocol is designed to ensure the ordered communication of the multiagent system. Finally, the effectiveness of the proposed algorithm is verified by numerical simulation. To solve the channel competition access problem of the sports intelligent learning system with special structure feedback delay model predictive control, a dual channel awareness scheduling strategy under the model predictive control framework was proposed, and the distributed threshold strategy of sensors and the priority threshold strategy of controllers were designed. It is proved that the sensor will eventually work at Nash equilibrium point under the policy updating mechanism, and the priority threshold strategy of the controller is better than the traditional independent and identically distributed access strategy. By avoiding the data transmission when the channel status is poor, the channel access of the system is efficient and saves energy.
通过文献资料法、逻辑分析法，基于人工智能在体育教育中的三种知识，即数据、逻辑和智能，研究了智能学习系统反馈延迟，结合移动通信导致的人工智能在线体育游戏教学、模式识别和虚拟技术与创新教学互动和体验相结合。促进绿色体育教学机器学习的发展，可以识别体育活动的类型，实现高效的体育学习诊断。智能决策支持系统可以识别体育人才，提高个性化体育教学评价的效果。从心理发展和教育的角度，考察了人工智能与体育教育融合需要解决的关键问题。然后，研究了具有网络诱导延迟和随机通信协议的非线性体育学习多智能体系统的反馈延迟的一致模型预测控制。在设计的通信等待机制下，每个智能体都有一定的延迟容忍度，并且可以通过确保系统的稳定性来确定该容忍度。同时，设计了一个随机通信协议，以确保多智能体系统的有序通信。最后，通过数值模拟验证了所提出算法的有效性。为了解决具有特殊结构反馈延迟模型预测控制的体育智能学习系统的信道竞争接入问题，提出了一种基于模型预测控制框架的双通道感知调度策略，设计了传感器的分布式门限策略和控制器的优先级门限策略。证明了在策略更新机制下，传感器最终将在纳什平衡点工作，并且控制器的优先级门限策略优于传统的独立同分布接入策略。通过避免信道状态较差时的数据传输，系统的信道接入高效且节能。
