Results 291 to 300 of about 6,738,298 (325)
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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 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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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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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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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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Theoretica Chimica Acta, 1995
An evaluation of the coulomb integral for a cuboid with uniform density is presented in analytic form, leading to the development of non-overlapping cube basis functions. The coulomb energy of the hydrogen molecule is determined with these functions fitted to the molecular orbital, and this result is compared with theab initio coulomb energy.
Michael E. Mura, Nicholas C. Handy
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An evaluation of the coulomb integral for a cuboid with uniform density is presented in analytic form, leading to the development of non-overlapping cube basis functions. The coulomb energy of the hydrogen molecule is determined with these functions fitted to the molecular orbital, and this result is compared with theab initio coulomb energy.
Michael E. Mura, Nicholas C. Handy
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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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2000
Abstract The aim of this chapter is to familiarize the reader with functional analytic tools which we will apply in the remaining parts of the book. We assume that the reader is already familiar with basic notions about topological spaces, semi-metric spaces and semi-normed spaces.
Johann Boos, Peter Cass
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Abstract The aim of this chapter is to familiarize the reader with functional analytic tools which we will apply in the remaining parts of the book. We assume that the reader is already familiar with basic notions about topological spaces, semi-metric spaces and semi-normed spaces.
Johann Boos, Peter Cass
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Structural Basis of Tetherin Function
Current HIV Research, 2012HIV-1 employs its structural proteins to orchestrate assembly and budding at the plasma membrane of host cells, which depends on numerous cellular factors. Although cells evolved interferon inducible restriction factors such as tetherin that act as a first line of defense, enveloped viruses, including HIV-1, developed countermeasures in the form of ...
Weissenhorn, Winfried +5 more
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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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Journal of Physics B: Atomic, Molecular and Optical Physics, 2004
A formalism to evaluate the performance of a given distributed basis (a basis formed by shifting a localized function) to span a given function is introduced. Various examples of distributed bases are given and their properties are discussed. The formalism is extended to the case of more than one basis function per site and examples are given.
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A formalism to evaluate the performance of a given distributed basis (a basis formed by shifting a localized function) to span a given function is introduced. Various examples of distributed bases are given and their properties are discussed. The formalism is extended to the case of more than one basis function per site and examples are given.
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