基于径向基函数神经网络的大学生体测成绩预测研究

Prediction of College Students'Physical Test Scores Based on Radial Basis Function Neural Network

大学生体质健康测试成绩是评价学生体质健康的重要标准,科学有效地对体测成绩进行预测分析是其他研究的基础。该研究运用径向基函数神经网络对某大学XX学院学生2022年体质健康测试数据进行预测和分析,并与BP神经网络、支持向量机等方法分类预测结果进行对比。试验结果表明,该预测模型具有较高的预测准确率和较好的泛化性能,为后续体育教师开展教学,相关学者开展研究提供了科学有效的分析方法。

The scores of physical fitness tests for college students are important indicators for evaluating their physical health.Scientific and effective prediction and analysis of physical fitness test scores serve as the basis for other research.In this paper,a prediction method for college student physical fitness test scores based on Radial Basis Function Neural Networks(RBFNN)is proposed.The RBFNN is used to predict and analyze the physical fitness test data of students of a certain university in 2022,and the classification prediction results are compared with those of Back Propagation Neural Network(BPNN),Support Vector Machines(SVM),and other methods.The experimental results demonstrate that the proposed prediction model based on RBFNN exhibits high prediction accuracy and good generalization performance for college student physical fitness test scores.It provides a scientifically effective analysis method for physical education teachers'teaching and researchers'subsequent studies.