Results 231 to 240 of about 60,572 (264)

Optimising the widths of radial basis functions

Proceedings 5th Brazilian Symposium on Neural Networks (Cat. No.98EX209), 2002
In the context of regression analysis with penalised linear models (such as RBF networks) certain model selection criteria can be differentiated to yield a re-estimation formula for the regularisation parameter such that an initial guess can be iteratively improved until a local minimum of the criterion is reached.
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Animated deformations with radial basis functions

Proceedings of the ACM symposium on Virtual reality software and technology - VRST '00, 2000
We present a novel approach to creating deformations of polygonal models using Radial Basis Functions (RBFs) to produce localized real-time deformations. Radial Basis Functions assume surface smoothness as a minimal constraint and animations produce smooth displacements of affected vertices in a model.
Jun-yong Noh, Douglas Fidaleo, Ulrich Neumann   +2 more
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Solving PDEs with radial basis functions

Acta Numerica, 2015
Finite differences provided the first numerical approach that permitted large-scale simulations in many applications areas, such as geophysical fluid dynamics. As accuracy and integration time requirements gradually increased, the focus shifted from finite differences to a variety of different spectral methods.
Bengt Fornberg, Natasha Flyer
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Radial Basis Functions

1995
Abstract The network models discussed in Chapters 3 and 4 are based on units which compute a non-linear function of the scalar product of the input vector and a weight vector. Here we consider the other major class of neural network model, in which the activation of a hidden unit is determined by the distance between the input vector and
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Computationally Efficient Radial Basis Function

2018
We introduced a Square-law based RBF kernel called SQuare RBF (SQ-RBF) which is computationally efficient and effective due to the elimination of the exponential term. In contrast to the Gaussian RBF, SQ-RBF requires smaller computational operation count and direct implementation without a call to higher order library.
Adedamola Wuraola, Nitish D. Patel
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Visualization of radial basis function networks

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
Presents a method for the 3D visualization of the structure of radial basis function networks. This method allows the visualization of basis function characteristics (centers and widths) along with second level weights. Network properties can be displayed simultaneously with the training data or test data in the same input space.
Adrian K. Agogino, Joydeep Ghosh, Cheryl E. Martin   +2 more
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Radial Basis Functions

2017
In this chapter the RBF mathematical concepts are exposed considering firstly the interpolation problem with the RBF function defined by known values at source points; a first hands-on example is provided showing how RBF work. Further topics of RBF theory are then introduced considering the differentiation of RBF, the fitting of an RBF with known ...
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Radial Basis Functions

2004
Radial basis functions are traditionaland powerful tools for multivariate scattered data interpolation.Much of the material presented in this chapter is essentially needed in the subsequent developments of this work,such as for the multi level approximation schemes in Chapter 5, and the mesh free simulation of transport processes in Chapter 6.
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Indexed families of functionals, and Gaussian radial basis functions

The 2002 45th Midwest Symposium on Circuits and Systems, 2002. MWSCAS-2002., 2003
We report on results concerning the capabilities of gaussian radial basis function networks in the setting of inner product spaces that need not be finite dimensional. Specifically, we show that important indexed families of functionals can be uniformly approximated, with the approximation uniform also with respect to the index.
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Radial Basis Functions: A Bayesian Treatment.

1998
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Barber, D., Schottky, B.
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