Optimisation of radial basis function classifiers using simulated annealing algorithm for cancer classification

2005 ◽  
Vol 41 (11) ◽  
pp. 630 ◽  
Author(s):  
H.-Q. Wang ◽  
D.-S. Huang ◽  
B. Wang
Keyword(s):  
Simulated Annealing ◽  
Radial Basis Function ◽  
Basis Function ◽  
Simulated Annealing Algorithm ◽  
Cancer Classification ◽  
Radial Basis ◽  
Annealing Algorithm ◽  
Radial Basis Function Classifiers

Technical data mining with evolutionary radial basis function classifiers

2009 ◽  
Vol 9 (2) ◽  
pp. 765-774 ◽  
Author(s):  
Markus Bauer ◽  
Oliver Buchtala ◽  
Timo Horeis ◽  
Ralf Kern ◽  
Bernhard Sick ◽  
...  
Keyword(s):  
Data Mining ◽  
Radial Basis Function ◽  
Basis Function ◽  
Technical Data ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

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.

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