scholarly journals A Cartesian grid technique based on one-dimensional integrated radial basis function networks for natural convection in concentric annuli

2008 ◽  
Vol 57 (12) ◽  
pp. 1709-1730 ◽  
Author(s):  
N. Mai-Duy ◽  
K. Le-Cao ◽  
T. Tran-Cong
Keyword(s):  
Natural Convection ◽  
Radial Basis Function ◽  
Basis Function ◽  
Cartesian Grid ◽  
Radial Basis Function Networks ◽  
One Dimensional ◽  
Radial Basis ◽  
Grid Technique

APPLICATION OF GENERALIZED RADIAL BASIS FUNCTION NETWORKS TO RECOGNITION OF RADAR TARGETS

1999 ◽  
Vol 13 (06) ◽  
pp. 945-962 ◽  
Author(s):  
DE-SHUANG HUANG
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Classification Performance ◽  
Kernel Functions ◽  
Radial Basis Function Networks ◽  
One Dimensional ◽  
Parzen Window ◽  
Window Functions ◽  
Radial Basis ◽  
Radar Targets

This paper extends general radial basis function networks (RBFN) with Gaussian kernel functions to generalized radial basis function networks (GRBFN) with Parzen window functions, and discusses applying the GRBFNs to recognition of radar targets. The equivalence between the RBFN classifiers (RBFNC) with outer-supervised signals of 0 or 1 and the estimate of Parzen windowed probabilistic density is proved. It is pointed out that the I/O functions of the hidden units in the RBFNC can be extended to general Parzen window functions (or called as potential functions). We present using recursive least square-backpropagation (RLS–BP) learning algorithm to train the GRBFNCs to classify five types of radar targets by means of their one-dimensional cross profiles. The concepts about the rate of recognition and confidence in the process of testing classification performance of the GRBFNCs are introduced. Six generalized kernel functions such as Gaussian, Double-Exponential, Triangle, Hyperbolic, Sinc and Cauchy, are used as the hidden I/O functions of the RBFNCs, and the classification performance of corresponding GRBFNCs for classifying one-dimensional cross profiles of radar targets is discussed.

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.

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