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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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Comparison of Radial Basis Functions
Numerical Analysis and Applications, 2018Summary: A survey of algorithms for approximation of multivariate functions with radial basis function (RBF) splines is presented. Algorithms of interpolating, smoothing, selecting the smoothing parameter, and regression with splines are described in detail.
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Applying radial basis functions
IEEE Signal Processing Magazine, 1996Discusses the application of neural networks to general and radial basis functions and in particular to adaptive equalization and interference rejection problems. Neural-network-based algorithms strike a good balance between performance and complexity in adaptive equalization, and show promise in spread spectrum systems.
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2001
In classical signal processing, we typically consider a single-input single-output (SISO) discretetime, causal, infinite-dimensional, time-invariant dynamical system described by y(t) = ∞σ k=p h(k)x(t - k)+ v(t) (3.1) where {h(k)} ε 1 is a weighting sequence, {v(t)} is a disturbance sequence of zero-mean, independent and identically distributed random ...
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In classical signal processing, we typically consider a single-input single-output (SISO) discretetime, causal, infinite-dimensional, time-invariant dynamical system described by y(t) = ∞σ k=p h(k)x(t - k)+ v(t) (3.1) where {h(k)} ε 1 is a weighting sequence, {v(t)} is a disturbance sequence of zero-mean, independent and identically distributed random ...
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Deformable Radial Basis Functions
2007Radial 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 0003 +1 more
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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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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 ...
Andrew R. Webb, Simon Shannon
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Visualization of radial basis function networks
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003Presents 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 +2 more
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