Study on Rock Bolt Support of Roadway of Coal Mine Using Neural Network

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Abstract:

The artificial neural network has been widely used in various field of science and engineering. The artificial neural network has marvelous ability to gain knowledge. In this paper, according to principle of artificial neural network , Model of artificial neural network of rock bolt support of roadway of coal mine has been constructed,Learning system of BP artificial neural network has been trained,it is shown by engineering application that artificial neural network can handle imperfect or incomplete data and it can capture nonlinear and complex relationships among variables of a system. the artificial neural network is emerging as a powerful tool for modeling with the complex system. Method and parameters of rock bolt support of roadway of coal mine can be predicated accurately using artificial neural network, that is of significance and valuable to those subjects of investigation and design of mining engineering

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3799-3802

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October 2013

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] Kohonen,T.Self-organization and associative memory,Series in Information Science, Vol. 8,Spring verlag,1984.

Google Scholar

[2] Hu shanxu, et al,Intuduction to Artificial Neural Network, Science press,Beijing,1994 (in Chines).

Google Scholar

[3] Grossberg,S.How does the brain build a cognitive code?Psychological Review,Vol. 87(1980),P: 1-51.

DOI: 10.1037/0033-295x.87.1.1

Google Scholar

[4] Hopfield,J.J.Neural networks with graded response have collective computational property like those of two state nerons,Proceedings of national academy of science,U.S.A., Vol. 89(1984),P: 3088-3092.

DOI: 10.1073/pnas.81.10.3088

Google Scholar

[5] Kurup, P. U. and N. K. Dudani. Neural networks for profiling stress history of clays from PCPT data. J. Geotechnical and Geoenvironmental Engineering 128(7)(2002),P: 569–578.

DOI: 10.1061/(asce)1090-0241(2002)128:7(569)

Google Scholar