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Radial Basis Function Networks
2018Radial basis function (RBF) networks represent a fundamentally different architecture from what we have seen in the previous chapters. All the previous chapters use a feed-forward network in which the inputs are transmitted forward from layer to layer in a similar fashion in order to create the final outputs.
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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, Hanspeter A. Mallot
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Radial Basis Functions Networks
2002The solution of complex mapping problems with artificial neural networks normally demands the use of a multi-layer network structure. This multi-layer topology process data into consecutive steps in each one of the layers. Radial Basis Functions networks are a particular neural network structure that uses radial functions in the intermediate, or hidden,
A. Braga +4 more
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Radial-Basis Function Networks
2000This chapter deals with a special class of artificial neural networks (ANNs) called radial-basis function (RBF) networks. These networks derive their structure and interpretation from the theory of interpolation in multidimensional spaces, and have a mathematical foundation imbedded in regularization theory for solving ill-conditioned problems.
Rao S. Govindaraju, Bin Zhang
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Multiscale Radial Basis Functions
2017Radial basis functions (RBFs) are a popular meshfree discretisation method for constructing high-order approximation spaces. They are used in various areas comprising, for example, scattered data approximation, computer graphics, machine learning, aeroelasticity and the geosciences.The approximation space is usually formed using the shifts of a fixed ...
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