Research on identification of coal and waste rock based on GLCM and BP neural network

When exploring the parameters for the coal and waste rock, eight characteristic parameters were selected as the whole characters of an image according to their significant differences in gray scale and texture features. On the basis, the error back-propagation algorithm of neural network is applied...

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Bibliographic Details
Main Authors: Liang Haonan, Su Baojin, He Yaqun, He Jingfeng, He Qiongqiong
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
BP neural network
characteristic parameters
Eigenvalues and eigenfunctions
gray-level co-occurrence matrix (GLCM)
MATLAB
Neurons
Signal processing
Signal processing algorithms
Sorting
Training
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Summary:When exploring the parameters for the coal and waste rock, eight characteristic parameters were selected as the whole characters of an image according to their significant differences in gray scale and texture features. On the basis, the error back-propagation algorithm of neural network is applied for the nonlinear identification of samples. The identification network was trained successfully through learning samples. Then, the validity of eight characteristic parameters was verified through the tests of experimental images. Meanwhile, the goal of intelligent identification of coal and waste rock is achieved successfully.
DOI:10.1109/ICSPS.2010.5555496