Results 251 to 260 of about 173,770 (295)

Hierarchical radial basis function networks

1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227), 2002
Ersoy (1991) and Ersoy and Hong (1990) have constructed a neural network architecture called the parallel, self-organizing, hierarchical neural network (PSHNN) that contains a number of stage neural networks. In their papers, the stage networks are one-layer networks with delta rule learning.
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Multiscale Approximation With Hierarchical Radial Basis Functions Networks

IEEE Transactions on Neural Networks, 2004
An approximating neural model, called hierarchical radial basis function (HRBF) network, is presented here. This is a self-organizing (by growing) multiscale version of a radial basis function (RBF) network. It is constituted of hierarchical layers, each containing a Gaussian grid at a decreasing scale.
S. Ferrari, M. Maggioni, N.A. Borghese
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Universal Approximation Using Radial-Basis-Function Networks

Neural Computation, 1991
There have been several recent studies concerning feedforward networks and the problem of approximating arbitrary functionals of a finite number of real variables. Some of these studies deal with cases in which the hidden-layer nonlinearity is not a sigmoid. This was motivated by successful applications of feedforward networks with nonsigmoidal hidden-
J, Park, I W, Sandberg
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Function Emulation Using Radial Basis Function Networks

Neural Networks, 1997
Abstract While learning an unknown input-output task, humans first strive to understand the qualitative structure of the function. Accuracy of performance is then improved with practice. In contrast, existing neural network function approximators do not have an explicit means for abstracting the qualitative structure of a target function.
Srinivasa V. Chakravarthy, Joydeep Ghosh
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Multi-layer radial basis function networks. An extension to the radial basis function

Proceedings of International Conference on Neural Networks (ICNN'96), 2002
This paper presents the initial research carried out into a new neural network called the multilayer radial basis function network (MRBF). The network extends the radial basis function (RBF) in a similar way to that in which the multilayer perceptron extends the perceptron.
R.J. Craddock, K. Warwick
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On Monotonic Radial Basis Function Networks

IEEE Transactions on Cybernetics
This article deals with monotonicity conditions for radial basis function (RBF) networks. Two architectures of RBF networks are considered-1) unnormalized network with a local character of the basis function and 2) a normalized network where the value of RBF is taken relatively with respect to the others.
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Evolutionary Radial Basis Function Networks

2018
Radial Basis Function (RBF) networks are one of the most popular and applied type of neural networks. RBF networks are universal approximators and considered as special form of multilayer feedforward neural networks that contain only one hidden layer with Gaussian based activation functions.
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Radial Basis Function Networks

2011
Age Eide, Thomas Lindblad, Guy Paillet
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Approximation by radial basis function networks

2003
We propose a method of function approximation by radial basis function networks. We will demonstrate that this approximation method can be improved by a pre-treatment of data based on a linear model. This approximation method will be applied to option pricing.
Amaury Lendasse, John. Lee, Eric De Bodt, Vincent Wertz, Michel Verleysen   +4 more
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Deep Radial Basis Function Networks

2021 7th International Conference on Contemporary Information Technology and Mathematics (ICCITM), 2021
Mohie M. Alqezweeni, Vladimir I. Gorbachenko, Dmitry A. Stenkin   +2 more
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