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The relationship between these basis functions found with the SVD and the normal concha mode shapes is demonstrated.
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Using totally 50 experimental observations, Radial Basis Function-based model was found with higher accuracy in predicting corrosion levels compared to Back Propagation-based model.
In the approximation history, many different basis functions have been found and studied.
We discuss the selection of basis functions set and find the optimal basis set.
We first present the relationship between the signal space and basis functions, and then find the basis function yielding the largest signal space.
The degree-three polynomial kernel and radial basis function kernels were found to work best.
The basis functions are specified with α = (m,n,G).
In this paper, we study the relation between the uniform B-spline basis functions with parameter and the B-spline basis functions.
From the Figure 19 it was found that the radial basis function network with a spread value of 4.31 showed good agreement.
In this figure, the basis functions are already multiplied with their corresponding spline parameter.
It is found that 9 basis functions for a rectangular plate give a converged solution, while 3 basis functions give pull-in parameters with an error of at most 4%.
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