Radial basis function

Known as: Basis, RBF, Radial basis functions 
A radial basis function (RBF) is a real-valued function whose value depends only on the distance from the origin, so that ; or alternatively on the… (More)
Wikipedia

Related topics

Related topics

31 relations
Activation functionArtificial neural networkBasis functionBoundary element method
(More)

Broader (2)

InterpolationNumerical analysis

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2010
Highly Cited
2010
Radial-Basis-Function-Based Neural Network for Harmonic Detection
The widespread application of power-electronic loads has led to increasing harmonic pollution in the supply system. In order to… (More)
  • figure 2
  • figure 1
  • table I
  • table II
  • figure 5
Is this relevant?
Highly Cited
2005
Highly Cited
2005
Image reconstruction algorithm for electrical capacitance tomography based on radial basis function neural network
Electrical capacitance tomography (ECT) technique is a new technique for two-phase flow measurement. This paper describes the… (More)
  • figure 1
  • figure 4
  • figure 3
  • figure 5
Is this relevant?
Highly Cited
2002
Highly Cited
2002
Face recognition with radial basis function (RBF) neural networks
A general and efficient design approach using a radial basis function (RBF) neural classifier to cope with small training sets of… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2001
Highly Cited
2001
Reconstruction and representation of 3D objects with radial basis functions
We use polyharmonic Radial Basis Functions (RBFs) to reconstruct smooth, manifold surfaces from point-cloud data and to repair… (More)
Is this relevant?
Highly Cited
2001
Highly Cited
2001
A Radial Basis Function Method for Global Optimization
We introduce a method that aims to nd the global minimum of a continuous nonconvex function on a compact subset of IR d. It is… (More)
Is this relevant?
Highly Cited
1995
Highly Cited
1995
Regularization in the Selection of Radial Basis Function Centers
Subset selection and regularization are two well-known techniques that can improve the generalization performance of… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • table 1
Is this relevant?
Highly Cited
1995
Highly Cited
1995
Error estimates and condition numbers for radial basis function interpolation
For interpolation of scattered multivariate data by radial basis functions, an \uncertainty relation" between the attainable… (More)
Is this relevant?
Highly Cited
1995
Highly Cited
1995
Regularisation in the Selection of Radial Basis Function
  • CentresMark, L. J., OrrCentre
  • 1995
Subset selection and regularisation are two well known techniques which can improve the generalisation performance of… (More)
  • figure 4
Is this relevant?
Highly Cited
1993
Highly Cited
1993
Approximation and Radial-Basis-Function Networks
This paper concerns conditions for the approximation of functions in certain general spaces using radial-basis-function networks… (More)
  • figure 1
Is this relevant?
Highly Cited
1993
Highly Cited
1993
Functional equivalence between radial basis function networks and fuzzy inference systems
It is shown that, under some minor restrictions, the functional behavior of radial basis function networks (RBFNs) and that of… (More)
Is this relevant?