Prediction and Analysis of College Sports Test Scores Based on Computational Intelligence

Abstract

Nowadays, colleges and universities are paying more and more attention to the physical condition of students. Many schools set up physical education courses to exercise students and improve their physical quality. They also conduct physical examinations every semester to test students’ conditions. In order to ensure more accurate sports results, this paper uses optimization of the neural group particle group model method to forecast the physical culture test scores of the investigated students. In addition, to guarantee accuracy the particle swarm optimization neural network model method, we compare the GXD method and the LM method with our method. It has the advantage of high precision, optimal prediction effect, strong versatility, higher recall rate, stronger antinoise performance, and wider application range. The article compares the neural network model method for particle swarm optimization with the GXD way and the LM way to ensure precision the neural network model method for particle swarm optimization.