The radial basis functions (RBFs) meshless method has high accuracy for the interpolation of scattered data in high dimensions. Most of the RBFs depend on a parameter, called shape parameter which plays a significant role to specify the accuracy of the RBF method. In this paper, we present three algorithms to choose the optimal value of the shape parameter. These are based on Rippa’s theory to remove data points from the data set and results obtained by Fasshauer and Zhang for the iterative approximate moving least square (AMLS) method. Numerical experiments confirm stable solutions with high accuracy compared to other methods.
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Yaghouti, M., Farshadmoghadam, F. (2023). Choosing the best value of shape parameter in radial basis functions by Leave-P-Out Cross Validation. Computational Methods for Differential Equations, 11(1), 108-129. doi: 10.22034/cmde.2022.46208.1939
Mohammad Reza Yaghouti; Farnaz Farshadmoghadam. "Choosing the best value of shape parameter in radial basis functions by Leave-P-Out Cross Validation". Computational Methods for Differential Equations, 11, 1, 2023, 108-129. doi: 10.22034/cmde.2022.46208.1939
Yaghouti, M., Farshadmoghadam, F. (2023). 'Choosing the best value of shape parameter in radial basis functions by Leave-P-Out Cross Validation', Computational Methods for Differential Equations, 11(1), pp. 108-129. doi: 10.22034/cmde.2022.46208.1939
Yaghouti, M., Farshadmoghadam, F. Choosing the best value of shape parameter in radial basis functions by Leave-P-Out Cross Validation. Computational Methods for Differential Equations, 2023; 11(1): 108-129. doi: 10.22034/cmde.2022.46208.1939