Application of Optimized BP Neural Network Model for the Training Load Prediction in Physical Education Teaching

Load prediction is mandatory for analyzing the working condition of the teachers when new courses are to be introduced or change in the learning environment. During complex environment, the seasonal prediction about sports has to be analyzed and designs the physical education training program for the students to increase the fitness of the students. In this study, load prediction in physical education is performed with the implementation of a back propagation neural network model with the support of genetic algorithm and termed as BPNNGA. The proposed prediction algorithm is compared with the existing algorithms such as back propagation neural network, K-means neural network, and random forest algorithm. The results proved that the proposed algorithm outperforms the existing algorithms with the accuracy percentage of 99%.