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

Author

Shen Yan ; Zhang Jing ; Sun Shuangshuang

Author_Institution

Coll. of Sci., Harbin Eng. Univ., Harbin, China

fYear

2012

fDate

6-8 Nov. 2012

Firstpage

298

Lastpage

301

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.

Keywords

condition monitoring; matrix algebra; neural nets; petroleum; pipelines; principal component analysis; production engineering computing; regression analysis; correlation coefficient matrix; matrix rotation-generalized regression neural network; petroleum pipeline data prediction; petroleum pipeline monitoring data; Correlation coefficient; Educational institutions; Matrix decomposition; Neural networks; Petroleum; Pipelines; Principal component analysis; correlation coefficient matrix; generalized regression neural network; matrix rotation; principal component analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems (GCIS), 2012 Third Global Congress on

Conference_Location

Wuhan

Print_ISBN

978-1-4673-3072-5

Type

conf

DOI

10.1109/GCIS.2012.17

Filename

6449539