Analysis of Sports Performance Prediction Model Based on GA-BP Neural Network Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2021; no. 1; p. 4091821
• Main Author: Wang, Jinjuan
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; Hindawi Limited
• Subjects: Accuracy; Algorithms; Athletes; Athletic Performance; Back propagation networks; Big Data; Data analysis; Data mining; Data processing; Exercise; Humans; Neural networks; Neural Networks, Computer; Performance prediction; Prediction models; Sports; Sports training; Students; Teaching; Training; Workloads
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2021/4091821

There are many factors that affect athletes’ sports performance in sports competitions. The traditional sports performance prediction method is difficult to obtain more accurate sports performance prediction results and corresponding data analysis in a short time, which is not conducive for coaches to formulate targeted and scientific training sprint plans for athletes’ problems. Therefore, based on GA-BP neural network algorithm, this paper constructs a sports performance prediction model and carries out experiments and analysis. The experimental results show that GA-BP neural network algorithm has a faster convergence speed than BP neural network and can achieve the expected error accuracy in a shorter time, which overcomes the problems of the BP neural network. At the same time, different from the previous models, GA-BP neural network algorithm can get the athlete training model according to the relationship between quality training indicators and special sports training results, which can more intuitively show the advantages and disadvantages of athletes. In the final sports performance prediction results, GA-BP neural network prediction results have higher accuracy, better stability, better prediction effect, and higher application value than BP neural network.