Optical Classification of Quartz Lascas by Artificial Neural Networks

被引：11

作者：

Fujiwara, Eric ^{[1
,2
]}

Marques Dos Santos, Murilo Ferreira ^{[1
]}

Schenkel, Egont Alexandre ^{[1
]}

Ono, Eduardo ^{[1
]}

Suzuki, Carlos Kenichi ^{[1
]}

机构：

[1] State Univ Campinas Unicamp, Lab Photon Mat & Devices, FEM DEMA, Campinas, SP, Brazil

[2] State Univ Campinas Unicamp, FEM DSI, Campinas, SP, Brazil

来源：

MINERAL PROCESSING AND EXTRACTIVE METALLURGY REVIEW | 2015年 / 36卷 / 05期

基金：

巴西圣保罗研究基金会;

关键词：

mineral gradation; quartz; silica glass; artificial neural networks;

D O I：

10.1080/08827508.2014.978315

中图分类号：

TF [冶金工业];

学科分类号：

0806 ;

摘要：

A gradation method based on quartz lascas (lumps) transparency level is proposed. The samples were irradiated by transmitting light, and the images histograms were processed by artificial neural networks. Additionally, the results were compared to conventional classification methods, including density and visual analysis. The network designed with backpropagation architecture using 4 hidden layers of 10 neurons yielded to a relative error <24% in relation to manual classification, indicating a good agreement to the miners criteria. Furthermore, the implementation of competitive learning with 5 neurons resulted in correct discrimination of samples regarding their optical characteristics with a completely non-subjective approach.

引用

页码：281 / 287

页数：7

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