Supervised and unsupervised learning in radial basis function classifiers

1994 ◽  
Vol 141 (4) ◽  
pp. 210 ◽  
Author(s):  
I. Tarassenko
Keyword(s):  
Radial Basis Function ◽  
Unsupervised Learning ◽  
Basis Function ◽  
Supervised And Unsupervised Learning ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

Technical data mining with evolutionary radial basis function classifiers

2009 ◽  
Vol 9 (2) ◽  
pp. 765-774 ◽  
Author(s):  
Markus Bauer ◽  
Oliver Buchtala ◽  
Timo Horeis ◽  
Ralf Kern ◽  
Bernhard Sick ◽  
...  
Keyword(s):  
Data Mining ◽  
Radial Basis Function ◽  
Basis Function ◽  
Technical Data ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

A NEW RBF NEURAL NETWORK FOR PREDICTION IN INDUSTRIAL CONTROL

2012 ◽  
Vol 11 (04) ◽  
pp. 749-775 ◽  
Author(s):  
ACHRAF JABEUR TELMOUDI ◽  
HATEM TLIJANI ◽  
LOTFI NABLI ◽  
MAARUF ALI ◽  
RADHI M'HIRI
Keyword(s):  
Radial Basis Function ◽  
Unsupervised Learning ◽  
Basis Function ◽  
Learning Algorithm ◽  
Radial Basis Function Network ◽  
Dynamic Monitoring ◽  
Industrial Control ◽  
Dynamic Memory ◽  
Input Layer ◽  
Radial Basis

A novel neural architecture for prediction in industrial control: the 'Double Recurrent Radial Basis Function network' (R2RBF) is introduced for dynamic monitoring and prognosis of industrial processes. Three applications of the R2RBF network on the prediction values confirmed that the proposed architecture minimizes the prediction error. The proposed R2RBF is excited by the recurrence of the output looped neurons on the input layer which produces a dynamic memory on both the input and output layers. Given the learning complexity of neural networks with the use of the back-propagation training method, a simple architecture is proposed consisting of two simple Recurrent Radial Basis Function networks (RRBF). Each RRBF only has the input layer with looped neurons using the sigmoid activation function. The output of the first RRBF also presents an additional input for the second RRBF. An unsupervised learning algorithm is proposed to determine the parameters of the Radial Basis Function (RBF) nodes. The K-means unsupervised learning algorithm used for the hidden layer is enhanced by the initialization of these input parameters by the output parameters of the RCE algorithm.

scholarly journals Comparing support vector machines with Gaussian kernels to radial basis function classifiers

1997 ◽  
Vol 45 (11) ◽  
pp. 2758-2765 ◽  
Author(s):  
B. Scholkopf ◽  
Kah-Kay Sung ◽  
C.J.C. Burges ◽  
F. Girosi ◽  
P. Niyogi ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Radial Basis Function ◽  
Basis Function ◽  
Support Vector ◽  
Gaussian Kernels ◽  
Vector Machines ◽  
Radial Basis ◽  
Radial Basis Function Classifiers

On the training of radial basis function classifiers

1992 ◽  
Vol 5 (4) ◽  
pp. 595-603 ◽  
Author(s):  
M.T. Musavi ◽  
W. Ahmed ◽  
K.H. Chan ◽  
K.B. Faris ◽  
D.M. Hummels
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Radial Basis ◽  
Radial Basis Function Classifiers
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