Coal classification method based on visible-infrared spectroscopy and an improved multilayer extreme learning machine

被引：36

作者：

Mao, Yachun ^{[1
]}

Le, Ba Tuan ^{[2
,3
]}

Xiao, Dong ^{[1
,2
]}

He, Dakuo ^{[2
]}

Liu, Chongmin ^{[2
]}

Jiang, Longqiang ^{[2
]}

Yu, Zhichao ^{[2
]}

Yang, Fenghua ^{[2
]}

Liu, Xinxin ^{[2
]}

机构：

[1] Northeastern Univ, Intelligent Mine Res Ctr, Shenyang 110819, Liaoning, Peoples R China

[2] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110819, Liaoning, Peoples R China

[3] Le Quy Don Tech Univ, Coll Control Technol, Hanoi 100000, Vietnam

来源：

OPTICS AND LASER TECHNOLOGY | 2019年 / 114卷

基金：

中国国家自然科学基金;

关键词：

IMPROVED RANDOM FOREST; IDENTIFICATION; QUANTIFICATION; REGRESSION;

D O I：

10.1016/j.optlastec.2019.01.005

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Coal classification is an indispensable task in coal mining and production. The traditional method of coal classification has the disadvantages of high cost, low speed and low accuracy. Therefore, a rapid coal classification method based on visible-infrared spectroscopy is proposed in this research. First, we collected samples of different coal types and used spectrometers to measure the spectral data of these samples. Then, we proposed an improved multilayer extreme learning machine algorithm using this algorithm to build a coal classification model. The simulation results showed that the model has good classification results. Compared with the traditional coal classification methods, this method has an unparalleled advantage in economy, speed and accuracy.

引用

页码：10 / 15

页数：6

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