Predicting Honors Student Performance Using RBFNN and PCA Method

Moke Xu, Yu Liang, Wenjun Wu

Abstract: This paper proposes a predictive model based on Principle Component Analysis (PCA) combining with radical basis function Neutral Network (RBFNN) to accurately predict performance of honors student through the analysis of personalized characteristics. This model consists of two phases: PCA is firstly adopted to apply dimension reduction to the honors student dataset; extracted principle features are then employed as the input of RBF Neutral Network so as to build a three-layer RFF Neutral Network predictive model. Compared with other Neutral Network models, the PCA-RBF predictive model demonstrates a faster convergence speed, a higher predictive accuracy and stronger generation ability. Moreover, this model enables honors programmer administrators to identify those honor students at early stage of risk, and allow their academic advisors to provide appropriate advising in a timely manner.