A Principal Component Analysis and Deep Back-Propagation Neural Network-based Approach to Gasoline Quality Prediction

Abstract

This paper proposes an approach that combines principal component analysis with a Deep Back-Propagation Neural Network model to solve high-latitude prediction problems. The approach is applied to establish a product quality prediction model for gasoline refinement. The simulation results have demonstrated effectiveness of the approach.