Results 311 to 320 of about 1,024,930 (374)

Spherical Radial Basis Functions

1998
Abstract In this chapter we deal with spherical radial basis functions, i.e. kernel functions depending only on the (spherical) distance between unit vectors. The importance of our considerations lies in the development of general Sobolev spaces ℋ and (invariant) pseudodifferential operators Λ in which it is possible that (i) the radial ...
W Freeden, T Gervens, M Schreiner
openaire   +1 more source

Median radial basis function neural network

IEEE Transactions on Neural Networks, 1996
Radial basis functions (RBFs) consist of a two-layer neural network, where each hidden unit implements a kernel function. Each kernel is associated with an activation region from the input space and its output is fed to an output unit. In order to find the parameters of a neural network which embeds this structure we take into consideration two ...
A G, Bors, I, Pitas
openaire   +2 more sources

Kernel Radial Basis Functions

2007
This paper introduces new kernel radial basis functions (RBFs) which characterize partial differential equation problems of interest. Then we developed the meshfree collocation methods based on these kernel RBFs to solve benchmark Helmholtz, modified Helmholtz, and convection-diffusion problems.
W. Chen, H. Wang, Q. H. Qin
openaire   +1 more source

Reconstruction and representation of 3D objects with radial basis functions

International Conference on Computer Graphics and Interactive Techniques, 2001
J. Carr, R. Beatson, J. Cherrie, T. J. Mitchell, W. R. Fright, B. C. McCallum, T. R. Evans   +6 more
semanticscholar   +1 more source

Radial Basis Function Networks

2018
Radial basis function (RBF) networks represent a fundamentally different architecture from what we have seen in the previous chapters. All the previous chapters use a feed-forward network in which the inputs are transmitted forward from layer to layer in a similar fashion in order to create the final outputs.
openaire   +1 more source

Deformable Radial Basis Functions

2007
Radial basis function networks (RBF) are efficient general function approximators. They show good generalization performance and they are easy to train. Due to theoretical considerations RBFs commonly use Gaussian activation functions. It has been shown that these tight restrictions on the choice of possible activation functions can be relaxed in ...
Wolfgang Hübner, Hanspeter A. Mallot
openaire   +1 more source

Radial Basis Functions Networks

2002
The solution of complex mapping problems with artificial neural networks normally demands the use of a multi-layer network structure. This multi-layer topology process data into consecutive steps in each one of the layers. Radial Basis Functions networks are a particular neural network structure that uses radial functions in the intermediate, or hidden,
A. Braga, A. C. Carvalho, T. Ludermir, M. de Almeida, E. Lacerda   +4 more
openaire   +1 more source

An 88-line MATLAB code for the parameterized level set method based topology optimization using radial basis functions

Structural And Multidisciplinary Optimization, 2018
P. Wei, Zuyu Li, Xueping Li, M. Wang
semanticscholar   +1 more source

Radial-Basis Function Networks

2000
This chapter deals with a special class of artificial neural networks (ANNs) called radial-basis function (RBF) networks. These networks derive their structure and interpretation from the theory of interpolation in multidimensional spaces, and have a mathematical foundation imbedded in regularization theory for solving ill-conditioned problems.
Rao S. Govindaraju, Bin Zhang
openaire   +1 more source

Influence of the Discretization Methods on the Distribution of Relaxation Times Deconvolution: Implementing Radial Basis Functions with DRTtools

, 2015
T. Wan, M. Saccoccio, Chi Chen, F. Ciucci   +3 more
semanticscholar   +1 more source