DocumentCode
820917
Title
A constructive method for multivariate function approximation by multilayer perceptrons
Author
Geva, Shlomo ; Sitte, Joaquin
Author_Institution
Fac. of Inf. Technol., Queensland Univ. of Technol., Brisbane, Qld., Australia
Volume
3
Issue
4
fYear
1992
fDate
7/1/1992 12:00:00 AM
Firstpage
621
Lastpage
624
Abstract
Mathematical theorems establish the existence of feedforward multilayered neural networks, based on neurons with sigmoidal transfer functions, that approximate arbitrarily well any continuous multivariate function. However, these theorems do not provide any hint on how to find the network parameters in practice. It is shown how to construct a perceptron with two hidden layers for multivariate function approximation. Such a network can perform function approximation in the same manner as networks based on Gaussian potential functions, by linear combination of local functions
Keywords
function approximation; neural nets; transfer functions; feedforward multilayered neural networks; multilayer perceptrons; multivariate function approximation; neurons; sigmoidal transfer functions; Australia; Function approximation; Information technology; Multi-layer neural network; Multilayer perceptrons; Neural networks; Neurons; Pathology; Shape control; Transfer functions;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.143376
Filename
143376
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