Feng Wei
Sanquan College of Xinxiang Medical University, Henan 453000, China.
Comput Intell Neurosci. 2022 Jul 7;2022:3544457. doi: 10.1155/2022/3544457. eCollection 2022.
A wireless sensor network (WSN) is a group of geographically scattered and specialized sensors to monitor and record variables related to environmental and storing the obtained data in a vital location. These networks have applications and can be utilized in different research domains including physical education where error prediction is assumed as one of the core issues. Thus, careful attention is required from the researcher to provide reliable and accurate prediction models. Thus, aiming the shortage of large prediction error in the physical education evaluation, which is based on the BP neural network and wireless sensor technology, a combination of AFP and questionnaire survey method is proposed in order to improve the accuracy and predictability of evaluation, according to the characteristics of different evaluation subjects. We select the evaluation index system as the input of wireless sensor technology and then use the principle of genetic algorithm to select the optimal individual and optimize the initial parameters of wireless sensor technology to establish the evaluation model of physical education quality. Through the training and testing of sample data, it is shown that the model greatly improves the accuracy of physical education quality evaluation and has a good application prospect in physical education evaluation.
无线传感器网络（WSN）是一组地理上分散的专用传感器，用于监测和记录与环境相关的变量，并将获取的数据存储在重要位置。这些网络有其应用领域，可用于不同的研究领域，包括体育教育，其中错误预测被视为核心问题之一。因此，研究人员需要格外注意，以提供可靠且准确的预测模型。鉴于基于BP神经网络和无线传感器技术的体育教育评价中存在较大预测误差的不足，根据不同评价对象的特点，提出了一种AFP与问卷调查方法相结合的方式，以提高评价的准确性和可预测性。我们选择评价指标体系作为无线传感器技术的输入，然后利用遗传算法的原理选择最优个体，优化无线传感器技术的初始参数，建立体育教育质量评价模型。通过对样本数据的训练和测试表明，该模型大大提高了体育教育质量评价的准确性，在体育教育评价中具有良好的应用前景。
