DocumentCode :
799167
Title :
Deterministic convergence of an online gradient method for BP neural networks
Author :
Wu, Wei ; Feng, Guorui ; Li, Zhengxue ; Xu, Yuesheng
Author_Institution :
Appl. Math. Dept., Dalian Univ. of Technol., China
Volume :
16
Issue :
3
fYear :
2005
fDate :
5/1/2005 12:00:00 AM
Firstpage :
533
Lastpage :
540
Abstract :
Online gradient methods are widely used for training feedforward neural networks. We prove in this paper a convergence theorem for an online gradient method with variable step size for backward propagation (BP) neural networks with a hidden layer. Unlike most of the convergence results that are of probabilistic and nonmonotone nature, the convergence result that we establish here has a deterministic and monotone nature.
Keywords :
backpropagation; convergence; deterministic algorithms; feedforward neural nets; gradient methods; backward propagation neural network; deterministic convergence; feedforward neural network; nonmonotone nature; online gradient method; probabilistic nature; Computer networks; Convergence; Defense industry; Feedforward neural networks; Gradient methods; H infinity control; Helium; Learning systems; Mathematics; Neural networks; Online gradient methods; backward propagation (BP) neural networks; convergence; Algorithms; Computer Simulation; Computer Systems; Neural Networks (Computer); Numerical Analysis, Computer-Assisted; Online Systems; Signal Processing, Computer-Assisted;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2005.844903
Filename :
1427759
Link To Document :
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