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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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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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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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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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Hermite Radial Basis Functions Implicits
Computer Graphics Forum, 2010Abstract The Hermite radial basis functions (HRBF) implicits reconstruct an implicit function which interpolates or approximates scattered multivariate Hermite data (i.e. unstructured points and their corresponding normals). Experiments suggest that HRBF implicits allow the reconstruction of surfaces rich in details and behave better than previous ...
Ives Macêdo +2 more
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A practical radial basis function equalizer
IEEE Transactions on Neural Networks, 1999A radial basis function (RBF) equalizer design process has been developed in which the number of basis function centers used is substantially fewer than conventionally required. The reduction of centers is accomplished in two-steps. First an algorithm is used to select a reduced set of centers that lie close to the decision boundary.
Jungsik Lee +2 more
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2003
In many areas of mathematics, science and engineering, from computer graphics to inverse methods to signal processing, it is necessary to estimate parameters, usually multidimensional, by approximation and interpolation. Radial basis functions are a powerful tool which work well in very general circumstances and so are becoming of widespread use as the
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In many areas of mathematics, science and engineering, from computer graphics to inverse methods to signal processing, it is necessary to estimate parameters, usually multidimensional, by approximation and interpolation. Radial basis functions are a powerful tool which work well in very general circumstances and so are becoming of widespread use as the
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Gradient Descent and Radial Basis Functions
2006In this paper, we present experiments comparing different training algorithms for Radial Basis Functions (RBF) neural networks. In particular we compare the classical training which consists of an unsupervised training of centers followed by a supervised training of the weights at the output, with the full supervised training by gradient descent ...
Mercedes Fernández-Redondo +2 more
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RBFA: Radial Basis Function Autoencoders
2019 IEEE Congress on Evolutionary Computation (CEC), 2019We are introducing a new variation of the existing autoencoder called Radial Basis Function Autoencoders (RBFA). This version employs radial symmetric functions, in the first step of encoding, to map the input data vectors into a new form. The transformation which relies on the similarity between the input data examples and predefined center points ...
Maisa Daoud +2 more
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