Application of Improved GM (1,1) Model in Prediction of Training Results of 100 Meter Race

In order to accurately predict the athletes' performance, this paper adopts the improved GM (1,1) model in combination with the athletes' performance characteristics. Considering the defects of the GM (1,1) model, the modelling process of the GM (1,1) model is improved by using exponential transformation preprocessing original data and dynamic generation coefficient reconstruction background value. The improved bee colony algorithm is used to solve the global optimal dynamic generation coefficient, and then the exponential transformation grey model optimized by the improved bee colony algorithm is established. The training results of athletes in a gymnasium in the north were tested. From the experimental results, it can be seen that the prediction effect of the model is significantly better than other models. The accuracy of training performance prediction is effectively improved. It can be better applied to the prediction of athletes' 100m training performance.

Automated summary

This paper proposes a new method for predicting athletes' training performance by combining the improved GM (1,1) model with the characteristics of athletes' performance. Experimental results show that this method has better prediction accuracy compared to other models.