File Name: radial basis functions theory and implementations .zip
The distance is usually Euclidean distance , although other metrics are sometimes used.
- Radial basis function network
- Radial Basis Functions Mesh Morphing
- Radial Basis Functions - Theory and Implementations
- Adaptive Methods for Center Choosing of Radial Basis Function Interpolation: A Review
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Radial basis function network
Radial basis functions provide powerful meshfree method for multivariate interpolation for scattered data. But both the approximation quality and stability depend on the distribution of the center set. Many methods have been constructed to select optimal center sets for radial basis function interpolation. A review of these methods is given. Four kinds of center choosing algorithms which are thinning algorithm, greedy algorithm, arclength equipartition like algorithm and k-means clustering algorithm are introduced with some algorithmic analysis. Unable to display preview. Download preview PDF.
Show all documents Radial basis functions versus geostatistics in spatial interpolations Abstract. A key problem in environmental monitoring is the spatial interpolation. The main current approach in spatial interpolation is geostatistical. Geostatistics is neither the only nor the best spatial interpolation method.
Radial Basis Functions Mesh Morphing
Radial basis functions RBFs based mesh morphing allows to adapt the shape of a computational grid onto a new one by updating the position of all its nodes. Usually nodes on surfaces are used as sources to define the interpolation field that is propagated into the volume mesh by the RBF. The method comes with two distinctive advantages that makes it very flexible: it is mesh independent and it allows a node wise precision. There are however two major drawbacks: large data set management and excessive distortion of the morphed mesh that may occur. The BHS minimizes the mesh distortion but it is computational intense as a dense linear system has to be solved whilist the WC2 leads to a sparse system easier to solve but which can lack in smoothness.
Radial Basis Functions - Theory and Implementations
In the field of mathematical modeling , a radial basis function network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation , time series prediction , classification , and system control. They were first formulated in a paper by Broomhead and Lowe, both researchers at the Royal Signals and Radar Establishment.
Networks with kernel functions ; Radial basis function approximation ; Radial basis function neural networks ; Regularization networks. Radial basis function networks are a means of approximation by algorithms using linear combinations of translates of a rotationally invariant function, called the radial basis function.
Adaptive Methods for Center Choosing of Radial Basis Function Interpolation: A Review
KOHN, M. The series publishes expositions on all aspects of applicable and numerical mathematics, with an emphasis on new developments in this fast-moving area of research. State-of-the-art methods and algorithms as well as modern mathematical descriptions of physical and mechanical ideas are presented in a manner suited to graduate research students and professionals alike. Sound pedagogical presentation is a prerequisite. It is intended that books in the series will serve to inform a new generation of researchers. Also in this series: 1.
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Radial basis functions: theory and implementations / Martin Buhmann. p. cm. – (Cambridge monographs on applied and computational mathematics; 12).