Fast algorithm for surface reconstruction from cloud data based RBF neural network

2008 ◽  
Vol 28 (2) ◽  
pp. 469-472 ◽  
Author(s):  
Miao HUANG
Keyword(s):  
Neural Network ◽  
Surface Reconstruction ◽  
Fast Algorithm ◽  
Rbf Neural Network ◽  
Cloud Data

Hole Filling Algorithm in Surface Reconstruction Based on Radial Basis Function Neural Network

2008 ◽  
Vol 392-394 ◽  
pp. 750-754
Author(s):  
X.M. Wu ◽  
Gui Xian Li ◽  
W.M. Zhao
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Radial Basis Function ◽  
Surface Reconstruction ◽  
Basis Function ◽  
Hole Filling ◽  
Cloud Data ◽  
Learning Speed ◽  
Radial Basis ◽  
Data Surface

Aiming at hole filling in points cloud data reconstruction, a novel neural network arithmetic was employed in abridged points cloud data surface reconstruction. Radial basis function neural network and simulated annealing arithmetic was combined. Global optimization feature of simulated annealing was employed to adjust the network weights, the arithmetic can keep the network from getting into local minimum. MATLAB program was compiled, experiments on abridged points cloud data have been done employing this arithmetic, the result shows that this arithmetic can efficiently approach the surface with 10-4 mm error precision, and also the learning speed is quick and hole filling algorithm is successful and the reconstruction surface is smooth. Different methods have been employed to do surface reconstruction in comparison, the results illustrate the error employed algorithmic proposed in the paper is little and converge speed is quick.

RBF Neural Network Arithmetic and Applications in Surface Interpolation Reconstruction

2011 ◽  
Vol 460-461 ◽  
pp. 575-580
Author(s):  
X.M. Wu ◽  
Gui Xian Li ◽  
De Bin Shan ◽  
G.B. Yu
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Rbf Neural Network ◽  
Nonlinear Function ◽  
Hole Filling ◽  
Cloud Data ◽  
Surface Interpolation ◽  
Learning Speed ◽  
Arbitrary Precision ◽  
Data Surface

Aiming at problems such as: surface interpolation reconstruction of points cloud data，surface hole filling and two simple surface connection, a neural network arithmetic was employed. Based on radial basis function neural network, simulated annealing was employed to adjust the network weights. The new arithmetic can approach any nonlinear function by arbitrary precision, and also keep the network from getting into local minimum for global optimization feature of simulated annealing. MATLAB program was compiled, experiments on points cloud data have been done employing this arithmetic, the result shows that this arithmetic can efficiently approach the surface with 10-4 mm error precision, and also the learning speed is quick and reconstruction surface is smooth.

Fault Surface Models of Coal Rake Based on RBF Neural Network

2014 ◽  
Vol 484-485 ◽  
pp. 616-619
Author(s):  
Xiao Li Pan ◽  
Li Hua Mu ◽  
Hui Chen
Keyword(s):  
Neural Network ◽  
Linear Function ◽  
Surface Reconstruction ◽  
High Precision ◽  
Rbf Neural Network ◽  
Coal Extraction ◽  
Coal Surface ◽  
Fault Surface ◽  
Surface Models ◽  
Cloud Of Points

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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