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• W2797001662 abstract "Approximation of scattered geometric data is often a task in many engineering problems. The Radial Basis Function (RBF) approximation is appropriate for large scattered (unordered) datasets in d-dimensional space. This method is useful for a higher dimension d>=2, because the other methods require a conversion of a scattered dataset to a semi-regular mesh using some tessellation techniques, which is computationally expensive. The RBF approximation is non-separable, as it is based on a distance of two points. It leads to a solution of overdetermined Linear System of Equations (LSE). In this paper a new RBF approximation method is derived and presented. The presented approach is applicable for d dimensional cases in general." @default.
• W2797001662 created "2018-04-24" @default.
• W2797001662 creator A5035524167 @default.
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• W2797001662 date "2018-04-18" @default.
• W2797001662 modified "2023-09-27" @default.
• W2797001662 title "A New Radial Basis Function Approximation with Reproduction." @default.
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• W2797001662 hasPublicationYear "2018" @default.
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