可见、近红外光谱和深度学习CNN-ELM算法的煤炭分类

doi:10.3964/j.issn.1000-0593(2018)07-2107-06

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Abstract Coal serves as the main energy in industrial field, the quality of which has a decisive effect on industry and environment. In the using process of coal, if the category of the coal fails to be identified correctly, it will result in great harm to production efficiency, environmental pollution and economical loss. The traditional way of classifying coal mainly depends on artificial classification as well as chemical analysis, which however entails high cost and consumes too much time. Therefore, it becomes more and more important to identify the quality of coal quickly and correctly. Hence, this essay comes up with the idea of combining deep learning, ELM arithmetic and visible, infrared spectra to construct coal classification model. Firstly, we collected different coal samples from Fushun, Yimin and Henan Jiajinkou coal mining area, and used the American Spectra Vista SVC HR-1024 spectrometer for the measurement of the spectral data. Then we used the deep learning of convolutional neural network-CNN to extract spectral characteristics, and adopted ELM arithmetic to construct classification model for spectral data. Finally, in order to further improve the classification accuracy, this article made use of particle swarm optimization algorithm by using a range of newly defined inertia weight and acceleration factor values to improve the particle swarm optimization algorithm. Then, we used the improved particle swarm optimization to optimize CNN-ELM networks. Experimental results from comparison between PCA and CNN network reveal CNN network as a better feature extraction method for the spectrum. The results also show that CNN-ELM classification model has a good classification effect. The improved ELM classification model accuracy is higher than that of the basic ELM and SVM classification model. Compared with the traditional chemical methods and artificial methods, this method has the advantage of being unparalleled in economy, speed and accuracy.

Key words：Visible，near-infrared spectroscopy; Coal; Convolutional neural network; Particle swarm optimization; Extreme learning machine

Received: 2017-07-11 Accepted: 2017-11-20

ZTFLH:

TD94

Corresponding Authors: XIAO Dong E-mail: xiaodong@ise.neu.edu.cn

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2018)07-2107-06 OR http://www.gpxygpfx.com/EN/Y2018/V38/I07/2107