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Radial Basis Functions
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  • Cited by 678
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Sarra, Scott A. 2018. Radial Basis Function methods-reduced computational expense by exploiting symmetry. Numerical Methods for Partial Differential Equations,

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    Oqielat, Moa’ath N. 2018. Scattered data approximation using radial basis function with a cubic polynomial reproduction for modelling leaf surface. Journal of Taibah University for Science, Vol. 12, Issue. 3, p. 331.

    Cavoretto, Roberto Schneider, Teseo and Zulian, Patrick 2018. OpenCL Based Parallel Algorithm for RBF-PUM Interpolation. Journal of Scientific Computing, Vol. 74, Issue. 1, p. 267.

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    Radial Basis Functions
    • Online ISBN: 9780511543241
    • Book DOI: https://doi.org/10.1017/CBO9780511543241
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Book description

In many areas of mathematics, science and engineering, from computer graphics to inverse methods to signal processing, it is necessary to estimate parameters, usually multidimensional, by approximation and interpolation. Radial basis functions are a powerful tool which work well in very general circumstances and so are becoming of widespread use as the limitations of other methods, such as least squares, polynomial interpolation or wavelet-based, become apparent. The author's aim is to give a thorough treatment from both the theoretical and practical implementation viewpoints. For example, he emphasises the many positive features of radial basis functions such as the unique solvability of the interpolation problem, the computation of interpolants, their smoothness and convergence and provides a careful classification of the radial basis functions into types that have different convergence. A comprehensive bibliography rounds off what will prove a very valuable work.

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