Grade Prediction Model Based on DeepCycle Neural Network Classification Algorithm

Abstract

There are many factors affecting students’ grades, which make the prediction of students’ grades present high dimensional and nonlinear characteristics. Therefore, the traditional method has a large error in the prediction results, which is difficult to meet the practical needs. With the rapid development of artificial neural network (Ann), the deep cycle neural network algorithm based on Ann provides a new approach for student achievement prediction. In order to further improve the accuracy of student achievement prediction, this paper proposes a performance prediction model based on deep cycle neural network algorithm. First, principal component analysis is used for data dimensionality reduction processing of the established student writing evaluation system, and the first five principal components are extracted. Then, these principal components are taken as the input of the neural network to construct a three-layer neural network prediction model. The experimental results show that, compared with the single RBF neural network and BP neural network, the prediction model under deep cycle neural network is simple in structure, fast in convergence and 21.6% higher in prediction accuracy, which verifies the effectiveness of the model proposed in this paper.

Prediction of College Students’ Sports Performance Based on Improved BP Neural Network

“…Backpropagation (BP) neural network is a highly complex and nonlinear dynamic analysis system proposed by Rumelhart and McClelland in 1986. It forms an interconnected network structure through various independent units [11][12][13][14][15]. e neural function learns the data samples, establishes the connection weights and thresholds between the units, and then deals with complex nonlinear problems without specific functional forms [16][17][18].…”