The Prediction of Petroleum Pipeline Data Based on Matrix Rotation-Generalized Regression Neural Network

Abstract

This paper presents a new approach for petroleum pipeline data prediction. In order to obtain correlation coefficient matrix of variables of petroleum pipeline monitoring data monthly, a novel method of matrix rotation-generalized regression neural network for petroleum pipeline data prediction is proposed. The simulation analysis demonstrates that the model is not only more precise, but also more effective and feasible.