Hierarchical radial basis function networks and local polynomial un-warping for X-ray image intensifier distortion correction: A comparison with global techniques

2003 ◽  
Vol 41 (2) ◽  
pp. 151-163 ◽  
Author(s):  
P. Cerveri ◽  
C. Forlani ◽  
A. Pedotti ◽  
G. Ferrigno
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Image Intensifier ◽  
Distortion Correction ◽  
Radial Basis Function Networks ◽  
X Ray ◽  
Local Polynomial ◽  
Radial Basis

Characterization of Concentrating Photovoltaic modules by cooperative competitive Radial Basis Function Networks

2013 ◽  
Vol 40 (5) ◽  
pp. 1599-1608 ◽  
Author(s):  
A.J. Rivera ◽  
B. García-Domingo ◽  
M.J. del Jesus ◽  
J. Aguilera
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Networks ◽  
Photovoltaic Modules ◽  
Radial Basis

Universal Approximation Using Radial-Basis-Function Networks

1991 ◽  
Vol 3 (2) ◽  
pp. 246-257 ◽  
Author(s):  
J. Park ◽  
I. W. Sandberg
Keyword(s):  
Finite Number ◽  
Radial Basis Function ◽  
Basis Function ◽  
Universal Approximation ◽  
Radial Basis Function Networks ◽  
Feedforward Networks ◽  
Rbf Networks ◽  
Smoothing Factor ◽  
Radial Basis ◽  
Hidden Layer

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-layer units. This paper reports on a related study of radial-basis-function (RBF) networks, and it is proved that RBF networks having one hidden layer are capable of universal approximation. Here the emphasis is on the case of typical RBF networks, and the results show that a certain class of RBF networks with the same smoothing factor in each kernel node is broad enough for universal approximation.

scholarly journals Novel Online Dimensionality Reduction Method with Improved Topology Representing and Radial Basis Function Networks

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0131631
Author(s):  
Shengqiao Ni ◽  
Jiancheng Lv ◽  
Zhehao Cheng ◽  
Mao Li
Keyword(s):  
Dimensionality Reduction ◽  
Radial Basis Function ◽  
Basis Function ◽  
Reduction Method ◽  
Radial Basis Function Networks ◽  
Radial Basis ◽  
Dimensionality Reduction Method

Forward selection radial basis function networks applied to bacterial classification based on MALDI-TOF-MS

Talanta ◽  
2004 ◽  
Vol 63 (3) ◽  
pp. 527-532 ◽  
Author(s):  
Zhuoyong Zhang ◽  
Dan Wang ◽  
Peter de B Harrington ◽  
Kent J Voorhees ◽  
Jon Rees
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis Function Networks ◽  
Forward Selection ◽  
Maldi Tof Ms ◽  
Maldi Tof ◽  
Bacterial Classification ◽  
Radial Basis ◽  
Tof Ms
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