DocumentCode
1365639
Title
A successive overrelaxation backpropagation algorithm for neural-network training
Author
De Leone, Renato ; Capparuccia, Rosario ; Merelli, Emanuela
Author_Institution
Dipartimento di Matematica e Fisica, Camerino Univ., Macerata, Italy
Volume
9
Issue
3
fYear
1998
fDate
5/1/1998 12:00:00 AM
Firstpage
381
Lastpage
388
Abstract
A variation of the classical backpropagation algorithm for neural network training is proposed, and convergence is established using the perturbation results of Mangasarian and Solodov (1994). The algorithm is similar to the successive overrelaxation (SOR) algorithm for systems of linear equations and linear complementary problems in using the most recently computed values of the weights to update the values on the remaining arcs
Keywords
backpropagation; convergence of numerical methods; neural nets; optimisation; perturbation techniques; backpropagation; convergence; learning; linear complementary problems; neural-network; optimisation; perturbation; successive overrelaxation; Backpropagation algorithms; Biological neural networks; Boolean functions; Convergence; Equations; Humans; Network topology; Pattern recognition; Proteins; Speech recognition;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.668881
Filename
668881
Link To Document