Physical function evaluation in volleyball training based on intelligent GRNN.

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