Guangdong University of Finance & Economics, Sports Dept, Guangzhou 510320, Guangdong, China.
Competitive Sports Academy, Guangdong Sports Vocational and Technical College, Guangzhou 510500, Guangdong, China.
Comput Intell Neurosci. 2022 Aug 24;2022:3693310. doi: 10.1155/2022/3693310. eCollection 2022.
With the rapid development of soft computing technology, various models and comprehensive analysis methods are emerging one after another, and new theories and research results continue to emerge, showing great strength and development potential in actual theoretical research and engineering applications. This paper analyzes the air pollution detection and environmental responsibility of sports clubs based on RBF neural networks, constructs the corresponding neural network algorithm, and simulates and analyzes the data. In the process of simulation design, we adjust the weight and threshold of the network according to the error performance of the network to realize the functions required by the system. Different models were used to predict the concentration of air pollutants in typical cities. At the same time, a meta-analysis method was used to conduct a preliminary discussion on the impact of air pollutants on the health of the Chinese population, and some research results were obtained. In the past years, Chinese sports clubs have also built a solid social environmental protection system around the related environmental protection responsibilities of sports clubs. The research on green environmental monitoring has improved people's awareness of environmental responsibility and provided technical support for the green development of sports clubs.
随着软计算技术的飞速发展，各种模型和综合分析方法层出不穷，新的理论和研究成果不断涌现，在实际理论研究和工程应用中表现出强大的实力和发展潜力。本文基于 RBF 神经网络分析了体育俱乐部的空气污染检测和环境责任，构建了相应的神经网络算法，并对数据进行了模拟分析。在模拟设计过程中，我们根据网络的误差性能调整网络的权重和阈值，以实现系统所需的功能。使用不同的模型来预测典型城市空气中污染物的浓度。同时，采用荟萃分析方法对空气污染物对中国人口健康的影响进行了初步探讨，得出了一些研究结果。在过去的几年中，中国的体育俱乐部也围绕体育俱乐部的相关环境责任建立了坚实的社会环境保护体系。绿色环境监测的研究提高了人们的环境责任意识，为体育俱乐部的绿色发展提供了技术支持。
