DocumentCode
1745030
Title
A backpropagation learning framework for feedforward neural networks
Author
Yu, Xinghuo ; Efe, M Onder ; Kaynak, Okyay
Author_Institution
Fac. of Inf. & Commun., Central Queensland Univ., Rockhampton, Qld., Australia
Volume
3
fYear
2001
fDate
6-9 May 2001
Firstpage
700
Abstract
In this paper, a general backpropagation learning framework for the training of feedforward neural networks is proposed. The convergence to global minimum under the framework is investigated using the Lyapunov stability theory. It is shown the existing feedforward neural network training algorithms are special cases of the proposed framework
Keywords
Lyapunov methods; backpropagation; convergence; feedforward neural nets; Lyapunov stability theory; backpropagation learning framework; convergence; feedforward neural networks; global minimum; training algorithms; Backpropagation algorithms; Convergence; Data mining; Feedforward neural networks; Function approximation; Informatics; Lyapunov method; Neural networks; Neurons; Predictive models;
fLanguage
English
Publisher
ieee
Conference_Titel
Circuits and Systems, 2001. ISCAS 2001. The 2001 IEEE International Symposium on
Conference_Location
Sydney, NSW
Print_ISBN
0-7803-6685-9
Type
conf
DOI
10.1109/ISCAS.2001.921407
Filename
921407
Link To Document