Results 271 to 280 of about 274,124 (332)

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.
openaire   +1 more source

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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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
openaire   +1 more source

Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 2019
In this paper, a fuzzy scalar radial basis function neural network is proposed, in order to overcome the limitation of traditional aerodynamic reduced-order models having difficulty in adapting to input variables with different orders of magnitude.
Xu Wang, J. Kou, Weiwei Zhang
semanticscholar   +1 more source

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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Functional equivalence between radial basis function networks and fuzzy inference systems

IEEE Trans. Neural Networks, 1993
J. Jang, Chuen-Tsai Sun
semanticscholar   +1 more source

Approximation and Radial-Basis-Function Networks

Neural Computation, 1993
Jooyoung Park, I. Sandberg
semanticscholar   +1 more source

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
openaire   +1 more source

Integrative oncology: Addressing the global challenges of cancer prevention and treatment

Ca-A Cancer Journal for Clinicians, 2022
Jun J Mao,, Msce, Lorenzo Cohen, Karen M Mustian   +2 more
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