A general backpropagation algorithm for feedforward neural networks learning

Author

Yu, Xinghuo ; Efe, M. Onder ; Kaynak, Okyay

Author_Institution

Fac. of Informatics & Commun., Central Queensland Univ., Rockhampton, Qld., Australia

Volume

13

Issue

1

fYear

2002

fDate

1/1/2002 12:00:00 AM

Firstpage

251

Lastpage

254

Abstract

A general backpropagation algorithm is proposed for feedforward neural network learning with time varying inputs. The Lyapunov function approach is used to rigorously analyze the convergence of weights, with the use of the algorithm, toward minima of the error function. Sufficient conditions to guarantee the convergence of weights for time varying inputs are derived. It is shown that most commonly used backpropagation learning algorithms are special cases of the developed general algorithm

Keywords

backpropagation; convergence; feedforward neural nets; time-varying systems; Lyapunov function approach; commonly used backpropagation learning algorithms; error function minima; feedforward neural network learning; general backpropagation algorithm; sufficient conditions; time varying inputs; Algorithm design and analysis; Backpropagation algorithms; Convergence; Delay effects; Feedforward neural networks; Hopfield neural networks; Lyapunov method; Network address translation; Neural networks; Stability;

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/72.977323

Filename

977323