Analysis on training and bootstrap error evaluation with different parameter values for radial basis function on noisy data

2017 ◽  
Author(s):  
Nur Soffiah Sahubar Ali ◽  
Ahmad Ramli ◽  
Nuzlinda Abdul Rahman
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Noisy Data ◽  
Error Evaluation ◽  
Radial Basis ◽  
Parameter Values

scholarly journals The Quasi-Optimal Radial Basis Function Collocation Method: A Technical Note

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Juan Zhang ◽  
Mei Sun ◽  
Enran Hou ◽  
Zhaoxing Ma
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Computational Cost ◽  
Technical Note ◽  
Function Parameter ◽  
Approximation Solution ◽  
Collocation Points ◽  
Radial Basis ◽  
Solution Accuracy ◽  
Parameter Values

The traditional radial basis function parameter controls the flatness of these functions and influences the precision and stability of approximation solution. The coupled radial basis function, which is based on the infinitely smooth radial basis functions and the conical spline, achieves an accurate and stable numerical solution, while the shape parameter values are almost independent. In this paper, we give a quasi-optimal conical spline which can improve the numerical results. Besides, we consider the collocation points in the Chebyshev-type which improves solution accuracy of the method with no additional computational cost.

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