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The set of basis functions given in (15) are used to find the following diagonal weighting coefficients: a i i = − ∑ j = 1, j ≠ i N a i j, i = 1, 2, …, N. (17).
The off-diagonal weighting coefficients for the first-order derivative are determined by using the set of basis functions given in (12) and the off-diagonal weighting coefficients of the first-order derivative are found as [32] a i j = L ( 1 ) ( x i ) ( x i − x j ) L ( 1 ) ( x j ), k = 1, 2, …, N, i ≠ j. (16).
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where ({beta }^{m}_{0}) are the coefficients of the model that are estimated to yield the best fit to the data, M is the number of sub-regions or the number of basis functions in the model, and h m (X) is the spline basis function given in (4).
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%.
For a circular plate, 3 basis functions give a converged solution while the pull-in parameters computed with 2 basis functions have an error of at most 3%.
where the Baskakov and beta basis functions are given in (1).
The closed-form expression of the basis functions is given in the Supplementary Information.
The quintic B-spline basis function is given in Sect. 2. The finite difference approximation for time discretization of the given problem is discussed in Sect.
Considering that blended basis functions have given promising results in multi-resolution-based image fusion, and wavelet basis which is a typical basis function is also widely used in CS, we propose to explore the application of CS image fusion based on blended basis functions.
The analogues of these results in the context of periodic basis function networks are given in Sect. 4.
Some interesting properties of the basis functions are given.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com