scholarly journals An enhanced clustering function approximation technique for a radial basis function neural network

2012 ◽  
Vol 55 (3-4) ◽  
pp. 286-302 ◽  
Author(s):  
H. Pomares ◽  
I. Rojas ◽  
M. Awad ◽  
O. Valenzuela
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Approximation Technique ◽  
Radial Basis ◽  
Function Approximation Technique

A MATLAB-Based Study on the Realization and Approximation Performance of RBF Neural Networks

2013 ◽  
Vol 325-326 ◽  
pp. 1746-1749 ◽  
Author(s):  
Shuo Ding ◽  
Xiao Heng Chang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Radial Basis Function ◽  
Basis Function ◽  
Function Approximation ◽  
Learning Algorithm ◽  
Nonlinear Function ◽  
Feed Forward ◽  
Feed Forward Network ◽  
Radial Basis

BP neural network is a kind of widely used feed-forward network. However its innate shortcomings are gradually giving rise to the study of other networks. Currently one of the research focuses in the area of feed-forward networks is radial basis function neural network. To test the radial basis function neural network for nonlinear function approximation capability, this paper first introduces the theories of RBF networks, as well as the structure, function approximation and learning algorithm of radial basis function neural network. Then a simulation test is carried out to compare BPNN and RBFNN. The simulation results indicate that RBFNN is simpler in structure, faster in speed and better in approximation performance. That is to say RBFNN is superior to BPNN in many aspects. But when solving the same problem, the structure of radial basis networks is more complicated than that of BP neural networks. Keywords: Radial basis function; Neural network; Function approximation; Simulation; MATLAB

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