Analysis of Anxiety and Stress Sources of Track and Field Athletes Based on Neural Networks
To enhance athletes'competitive performance,it is essential to accurately gauge their anxiety levels and adjust their mental state accordingly.A hierarchical clustering algorithm has been employed to pinpoint sources of stress among track and field athletes,and an athlete anxiety analysis model based on the RBF(Radial Basis Function)model has been developed.This model captures the characteristics of various anxiety states through hidden unit centers,function widths,and connection weights.Testing has demonstrated that this model is both highly accurate and efficient,enabling precise classification and identification of different anxiety states.This provides a robust foundation for making informed decisions on managing the stress experienced by track and field athletes.
analytic hierarchy processstressanxietytrack and field athletes
万方数据
维普
