Results 231 to 240 of about 541,115 (266)

Shape-adaptive radial basis functions

IEEE Transactions on Neural Networks, 1998
Radial basis functions for discrimination and regression have been used with some success in a wide variety of applications. Here, we investigate the optimal choice for the form of the basis functions and present an iterative strategy for obtaining the function in a regression context using a conjugate gradient-based algorithm together with a ...
A R, Webb, S, Shannon
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Radial Basis Functions

Acta Numerica, 2000
Radial basis function methods are modern ways to approximate multivariate functions, especially in the absence of grid data. They have been known, tested and analysed for several years now and many positive properties have been identified. This paper gives a selective but up-to-date survey of several recent developments that explains their ...
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Radial Basis Functions

2013
The traditional basis functions in numerical PDEs are mostly coordinate functions, such as polynomial and trigonometric functions, which are computationally expensive in dealing with high dimensional problems due to their dependency on geometric complexity.
Chen, Wen, Fu, Zhuo Jia, Chen, C. S.
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STOCHASTIC RADIAL BASIS FUNCTIONS

International Journal of Neural Systems, 2001
Stochastic signal processing can implement gaussian activation functions for radial basis function networks, using stochastic counters. The statistics of neural inputs which control the increment and decrement operations of the counter are governed by Bernoulli distributions. The transfer functions relating the input and output pulse probabilities can
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Robustness of radial basis functions

Neurocomputing, 2005
Neural networks are intended to be used in future nanoelectronic technology since these architectures seem to be robust to malfunctioning elements and noise in its inputs and parameters. In this work, the robustness of radial basis function networks is analyzed in order to operate in noisy and unreliable environment.
Eickhoff, Ralf, Rückert, Ulrich
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Radial Basis Functions

2014
This last chapter may be seen as a general introduction to meshless methods, not only for surface reconstruction but in other problems of scattered data. The general interpolation and smoothing problems are described in terms of radial basis functions; we then illustrate some characterizations of these functions.
Hebert Montegranario, Jairo Espinosa
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On radial basis functions

2019
Many sciences and other areas of research and applications from engineering to economics require the approximation of functions that depend on many variables. This can be for a variety of reasons. Sometimes we have a discrete set of data points and we want to find an approximating function that completes this data; another possibility is that precise ...
Buhmann, Martin, Jäger, Janin
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Radial Basis Function Networks

2013
Learning is an approximation problem, which is closely related to the conventional approximation techniques, such as generalized splines and regularization techniques. The RBF network has its origin in performing exact interpolation of a set of data points in a multidimensional space [81].
Ke-Lin Du, M. N. S. Swamy
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Adaptive radial basis functions

Proceedings of 13th International Conference on Pattern Recognition, 1996
We develop adaptive radial basis functions: kernel-based models for regression and discrimination where the functional form of the basis function depends on the data. The approach may be regarded as a radial form of projection pursuit, with the additional constraint that the basis functions have a common functional form.
A.R. Webb, S. Shannon
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