Abstract
In the process of using coal, if the type of coal cannot be accurately determined, it will have a significant impact on production efficiency, environmental pollution, and economic loss. At present, the traditional classification method of coal mainly relies on technician’s experience. This requires a lot of manpower and time, and it is difficult to automate. This paper mainly studies the application of visible-infrared spectroscopy and machine learning methods in coal mine identification and analysis to provide guidance for coal mining and production. This paper explores a fast and high-precision method for coal identification. In this paper, for the characteristics of high dimensionality, strong correlation, and large redundancy of spectral data, the local receptive field (LRF) is used to extract the advanced features of spectral data, which is combined with the extreme learning machine (ELM). We improved the coyote optimization algorithm (COA). The improved coyote optimization algorithm (I-COA) and local receptive field-based extreme learning machine (ELM-LRF) are used to optimize the structure and training parameters of the extreme learning machine network. The experimental results show that the coal classification model based on the network and visible–infrared spectroscopy can effectively identify the coal types through the spectral data. Compared with convolutional neural networks (CNN algorithm) and principal component analysis (PCA algorithm), LRF can extract the spectral characteristics of coal more effectively.
Highlights
Coal is the main source of energy in the world
Compared with convolutional neural networks (CNN algorithm) and principal component analysis (PCA algorithm), LRF can extract the spectral characteristics of coal more effectively
In view of the characteristics of high dimensionality, strong correlation, and high redundancy of spectral data, this paper proposes to combine the local receptive field with Extreme learning machine (ELM) to solve the problem of coal classification, and to further improve the classification accuracy, the coyote optimization algorithm is studied
Summary
Coal is the main source of energy in the world. With the development of society and industry, the analytical quality of coal plays a decisive role in production efficiency and environmental pollution. In the current mining process, the traditional coal classification method mainly relies on artificial experience classification This requires a lot of manpower and material resources, and it is difficult to achieve automation. Connected ELM has good generalization performance in many applications and achieves high efficiency In applications such as spectral analysis and image processing, there may be strong local correlations, so it is reasonable to expect that the corresponding neural network has local connections instead of full connections in order to learn local correlations. In view of the characteristics of high dimensionality, strong correlation, and high redundancy of spectral data, this paper proposes to combine the local receptive field with ELM to solve the problem of coal classification, and to further improve the classification accuracy, the coyote optimization algorithm is studied.
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