Fault Surface Models of Coal Rake Based on RBF Neural Network

Xiao Li Pan; Li Hua Mu; Hui Chen

doi:10.4028/www.scientific.net/AMM.484-485.616

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 484-485Fault Surface Models of Coal Rake Based on RBF...

Fault Surface Models of Coal Rake Based on RBF Neural Network

Abstract:

In order to improve the accuracy of prospecting and efficiency of coal extraction, it is necessary to understand the geological construction deeply. Therefore, the reconstruction of fault surface models is highly important. Reconstructe surface from an unorganized cloud of points by using the RBF neural networkcs advantages of approximating no-linear function, and the algorithmcs scheme and analyses were given and the proposed method was applied to the coal surface reconstruction, this neural network can not only approximate the surface with high precision but also has good smoothness.

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Periodical:

Applied Mechanics and Materials (Volumes 484-485)

Pages:

616-619

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.484-485.616

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Online since:

January 2014

Authors:

Xiao Li Pan, Li Hua Mu, Hui Chen

Keywords:

Fault Plane, Neural Network (NN), Radial Basis Function (RBF), Surface Reconstruction

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References

[1] B. Fornberg, G. Wright. Stable computation of multiquadric interpolants for all values of the shape parameter. Comput. Math. Appl. 2004, 16: 497-523.

DOI: 10.1016/j.camwa.2003.08.010

Google Scholar

[2] E. Kaansa, Y. C. Hon. Circumventing the ill- conditioning problem with multiquadric radial basis functions: Applications to elliptic partial differential equations. Comput. Math. Appl. 2000, 39(7/8): 123-137.

DOI: 10.1016/s0898-1221(00)00071-7

Google Scholar

[3] Tang Ting, Tang Jjin, Bao Hujun, et a. l RBF-based constrained texturemapping. Computers& Graphics, 2003, 27 (1): 415-422.

DOI: 10.1016/s0097-8493(03)00036-0

Google Scholar

[4] Frey P J. Generation and adaptation of computational surface meshes from discrete anatomical data. International Journal for Numerical Methods in Engineering, 2004, 60(2): 1049-1074.

DOI: 10.1002/nme.992

Google Scholar

[5] Liu Xumin, Huang Houkuan, Xu Weixiang. Approximate B-spline surface based on RBF neural networks. ICCS 2005(LNCS 3514): 995-1002.

DOI: 10.1007/11428831_124

Google Scholar

[6] Chen Jianjun, Zheng Yao. R edes ign of a con form al boundary recovery algorithm for 3D Delaunay triangu lation[ J]. Journ al of Zhejiang S cience A. 2006, 7(12): 2031- (2042).

Google Scholar