Evaluation method for psychological resilience of athletes in high-intensity sports training based on evolutionary neural network

Abstract

To enhance the psychological resilience of athletes, a method for evaluating the psychological resilience of High-intensity Interval Training (HIIT) athletes based on evolutionary neural networks is studied. From the six criteria of frustration coping, personal characteristics, self-promotion, self-regulation, internal protection and external protection, the evaluation index of psychological resilience of athletes in sports High-intensity Interval Training is selected; the audition indicators are qualitatively analyzed according to the principle of indicator selection, and the indicators that do not meet the requirements are eliminated; Cluster analysis and coefficient of variation analysis are used to carry out quantitative analysis on the remaining evaluation indicators after qualitative analysis; the indicators after quantitative analysis are improved, to build the assessment index system of psychological resilience of athletes in high-intensity sports training. The Back Propagation (BP) neural network is optimized by a genetic algorithm, and the evolutionary neural network is constructed. The index data set is input into the evolutionary neural network as a sample, and the index weight value is output through training. The evaluation result and corresponding evaluation grade are determined based on the index weight value and membership degree. The experimental results show that when the number of hidden layers is 3, the calculation of evaluation index weights is the best; The weight of personal traits obtained from the evaluation results is the highest (0.206), while the weight of external protection is the lowest (0.151), and the evaluation results are basically consistent with the expert results. The above results show that this method can accurately evaluate the psychological resilience of athletes and significantly enhance their psychological resilience.