Radial basis functions : theory and implementations /

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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 circ...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Buhmann, M. D. (Martin Dietrich), 1963-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cambridge ; New York : Cambridge University Press, 2003.
Colección:Cambridge monographs on applied and computational mathematics ; 12.
Temas:
Radial basis functions.
Fonctions à base radiale.
MATHEMATICS > General.
Radial basis functions
Numerieke methoden.
Aproximação.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 1. Introduction
  • 1.1 Radial basis functions
  • 1.2 Applications
  • 1.3 Contents of the book
  • 2. Summary of methods and applications
  • 2.1 Invertibility of interpolation matrices
  • 2.2 Convergence analysis
  • 2.3 Interpolation and convergence
  • 2.4 Applications to PDEs
  • 3. General methods for approximation and interpolation
  • 3.1 Polynomial schemes
  • 3.2 Piecewise polynomials
  • 3.3 General nonpolynomial methods
  • 4. Radial basis function approximation on infinite grids
  • 4.1 Existence of interpolants
  • 4.2 Convergence analysis
  • 4.3 Numerical properties of the interpolation linear system
  • 4.4 Convergence with respect to parameters in the radial functions
  • 5. Radial basis functions on scattered data
  • 5.1 Nonsingularity of interpolation matrices
  • 5.2 Convergence analysis
  • 5.3 Norm estimates and condition numbers of interpolation matrices
  • 6. Radial basis functions with compact support
  • 6.1 Introduction
  • 6.2 Wendland's functions
  • 6.3 Another class of radial basis functions with compact support
  • 6.4 Convergence
  • 6.5 A unified class
  • 7. Implementations
  • 7.1 Introduction
  • 7.2 The BFGP algorithm and the new Krylov method
  • 7.3 The fast multipole algorithm
  • 7.4 Preconditioning techniques
  • 8. Least squares methods
  • 8.1 Introduction to least squares
  • 8.2 Approximation order results
  • 8.3 Discrete least squares
  • 8.4 Implementations
  • 8.5 Neural network applications
  • 9. Wavelet methods with radial basis functions
  • 9.1 Introduction to wavelets and prewavelets
  • 9.2 Basic definitions and constructions
  • 9.3 Multiresolution analysis and refinement
  • 9.4 Special constructions
  • 10. Further results and open problems
  • 10.1 Further results
  • 10.2 Open problems
  • Appendix: Some essentials on Fourier transforms.

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