DocumentCode
398078
Title
Feedforward neural networks with sigmoidal and radial basis functions hidden neurons
Author
Valova, Iren ; Gueorguieva, N. ; Kempka, Matthias
Author_Institution
Comput. & Inf. Sci., Massachusetts Univ., North Dartmouth, MA, USA
Volume
2
fYear
2003
fDate
5-8 Oct. 2003
Firstpage
1630
Abstract
This discussion paper concentrates on a possible combination of different activation functions in the multilayered perceptron (MLP) for classification tasks. The activation functions, which are combined are the sigmoidal and the radial basis function. We propose this approach to modification of MLP as solutions to some of the shortfalls of this network.
Keywords
benchmark testing; multilayer perceptrons; radial basis function networks; transfer functions; activation functions; benchmark tests; feedforward neural networks; intertwined spiral problem; multilayered perceptron; radial basis function neural networks; sigmoidal hidden neurons; Computer networks; Computer science; Convergence; Educational institutions; Feedforward neural networks; Feedforward systems; Multilayer perceptrons; Neural networks; Neurons; Testing;
fLanguage
English
Publisher
ieee
Conference_Titel
Systems, Man and Cybernetics, 2003. IEEE International Conference on
ISSN
1062-922X
Print_ISBN
0-7803-7952-7
Type
conf
DOI
10.1109/ICSMC.2003.1244646
Filename
1244646
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