Athlete performance prediction using discrete neural networks based on Event-group training theory

Training performance of athletes relies on the training initiative, which in turn is related to the construction of training plans. To forecast training performance of athletes, based on the Event-group training theory, this paper proposed a novel forecast method through combining with the discrete Hopfield neural network and wavelet function. The critical principle is that using Event-group training theory to construct 13 training indicators for athletes. The training scores of athletes is calculated by the constructed 13 training indicators. According to the designed training indicators, the discrete Hopfield neural network with wavelet function is implemented. In process of model training, to complete quick convergence and to guarantee the stability of state update to our discrete Hopfield neural network, the designed wavelet function is taken. Following that, the proposed model is trained by using the experimental dataset, and using the trained model to forecast the training performance of athletes. Experimental results show that the proposed model not only accurately forecasts the training performance of athletes, but also outperformed the comparative models in forecast accuracy. Results also show that the running efficiency of the proposed model won major competitors. The value of the designed training indicators not only finds these major factors affecting training performance of athletes, but also assists coaches in scientifically specifying training plans and observing athletes’ training performance.