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<p>This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. Three heterogeneous kernel functions, namely Gaussian kernel, radial basis kernel, and Matern kernel, are introduced, and a local weighted response mechanism is constructed to enhance the expression ability of nonlinear physiological signals. Second, a dynamic adjustment mechanism for smoothing factors based on local gradient perturbation is proposed, enabling the model to have response compression capability in high-fluctuation samples. Finally, combining the structure embedding mapping mechanism with a multi-scale linear compression framework, the reconstruction of high-dimensional physiological indicators and the elimination of redundant features are achieved, improving model deployment efficiency. Comparative experiments conducted on training data of a high-level university men’s volleyball team show that VSGRNN has a goodness-of-fit R<sup>2</sup> = 0.927 on the validation set, with a Root Mean Square Error (RMSE) only 1.68 and Symmetric Mean Absolute Percentage Error (SMAPE) controlled at 8.21%. Within the local perturbation interval, the peak response deviation is 6.7%, far better than the comparative models (Long Short-Term Memory (LSTM) + Attention at 8.5% and Tabular Data Network (TabNet) at 9.8%). When compressed to 30% of the original feature dimension, the error only increases by 7.9%, and the inference time is shortened by 46.1%. The research conclusion shows that VSGRNN outperforms traditional models in terms of accuracy, robustness, structural compression adaptability, and real-time feedback capability. This study provides an engineerable structure-response modeling method for the intelligent evaluation of physical functions in volleyball-specific training, which has high practical application value.</p>

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Physical function evaluation in volleyball training based on intelligent GRNN

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月17日

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  • Kaiyuan Dong,
  • Borhannudin bin Abdullah,
  • Hazizi bin Abu Saad,
  • Chenxi Lu

摘要

This study aims to improve both the evaluation accuracy and the real-time feedback capability in monitoring athletes’ physical function changes during volleyball training. Firstly, based on the framework of the generalized regression neural network, a variable-structure generalized regression neural network (VSGRNN) is proposed and developed. Three heterogeneous kernel functions, namely Gaussian kernel, radial basis kernel, and Matern kernel, are introduced, and a local weighted response mechanism is constructed to enhance the expression ability of nonlinear physiological signals. Second, a dynamic adjustment mechanism for smoothing factors based on local gradient perturbation is proposed, enabling the model to have response compression capability in high-fluctuation samples. Finally, combining the structure embedding mapping mechanism with a multi-scale linear compression framework, the reconstruction of high-dimensional physiological indicators and the elimination of redundant features are achieved, improving model deployment efficiency. Comparative experiments conducted on training data of a high-level university men’s volleyball team show that VSGRNN has a goodness-of-fit R2 = 0.927 on the validation set, with a Root Mean Square Error (RMSE) only 1.68 and Symmetric Mean Absolute Percentage Error (SMAPE) controlled at 8.21%. Within the local perturbation interval, the peak response deviation is 6.7%, far better than the comparative models (Long Short-Term Memory (LSTM) + Attention at 8.5% and Tabular Data Network (TabNet) at 9.8%). When compressed to 30% of the original feature dimension, the error only increases by 7.9%, and the inference time is shortened by 46.1%. The research conclusion shows that VSGRNN outperforms traditional models in terms of accuracy, robustness, structural compression adaptability, and real-time feedback capability. This study provides an engineerable structure-response modeling method for the intelligent evaluation of physical functions in volleyball-specific training, which has high practical application value.