DocumentCode
2435814
Title
The causes for premature saturation with backpropagation training
Author
Vitela, Javier E. ; Reifman, Jaques
Author_Institution
Inst. de Ciencias Nucl., Univ. Nacional Autonoma de Mexico, Mexico City, Mexico
Volume
3
fYear
1994
fDate
27 Jun-2 Jul 1994
Firstpage
1449
Abstract
The mechanism that causes the output nodes of feedforward multilayer networks mapped with sigmoid functions to prematurely saturate during backpropagation training is described. The necessary conditions for the occurrence of this undesirable phenomenon are also presented. Simulation results demonstrate the adequacy of the presented necessary conditions
Keywords
backpropagation; feedforward neural nets; multilayer perceptrons; backpropagation training; feedforward multilayer networks; necessary conditions; output nodes; premature saturation; sigmoid functions; Algorithm design and analysis; Backpropagation algorithms; Convergence; Inductors; Iterative algorithms; Joining processes; Laboratories; Nonhomogeneous media;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location
Orlando, FL
Print_ISBN
0-7803-1901-X
Type
conf
DOI
10.1109/ICNN.1994.374499
Filename
374499
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