RECURRENT NEURAL NETWORKS TO ANALYZE THE QUALITY OF NATURAL GAS

被引：3

作者：

Brokarev, I. A. ^{[1
]}

Farkhadov, M. P. ^{[2
]}

Vaskovskii, S. V. ^{[2
]}

机构：

[1] Natl Univ Oil & Gas, Gubkin Univ, Moscow, Russia

[2] Russian Acad Sci, Tech Sci, VA Trapeznikov Inst Control Sci, Moscow, Russia

来源：

VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE | 2021年 / 55期

关键词：

recurrent neural networks; natural gas quality analysis; gated recurrent unit;

D O I：

10.17223/19988605/55/2

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Comparative analysis of various neural network models was carried out for natural gas quality analysis. Based on the results of such analysis, it was concluded that recurrent neural networks are main statistical models in this problem. This paper considers a recurrent neural network with a more complex architecture. The neural network with gated recurrent unit is used in the discussed task in particular. The comparison of the main recurrent neural network models (simple recurrent neural network, recurrent neural network with long short-term memory, recurrent neural network with gated recurrent unit) is shown. Models accuracy characteristics are shown for analyzing the models performance.

引用

页码：11 / 17

页数：7

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