Results 221 to 230 of about 763,332 (302)
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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 ...
M. Biancolini
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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 ...
M. Biancolini
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Handbook of Neural Network Signal Processing, 2001
This chapter considers radial basis function (RBF) networks. A RBF network can be described as a parametrized model used to approximate an arbitrary function by means of a linear combination of basic functions. RBF networks belong to the class of kernel function networks where the inputs to the model are passed through kernel functions which limit the ...
A. Back
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This chapter considers radial basis function (RBF) networks. A RBF network can be described as a parametrized model used to approximate an arbitrary function by means of a linear combination of basic functions. RBF networks belong to the class of kernel function networks where the inputs to the model are passed through kernel functions which limit the ...
A. Back
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Shape-adaptive radial basis functions
IEEE Transactions on Neural Networks, 1998Radial 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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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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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, 2001Stochastic 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, 2005Neural 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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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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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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Kolmogorov-Arnold Networks are Radial Basis Function Networks
arXiv.orgThis short paper is a fast proof-of-concept that the 3-order B-splines used in Kolmogorov-Arnold Networks (KANs) can be well approximated by Gaussian radial basis functions.
Ziyao Li
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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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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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