DocumentCode
2234446
Title
A simple procedure in back-propagation training
Author
Yu, Chien-Cheng ; Liu, Bin-Da
Author_Institution
Dept. of Electr. Eng., Cheng Kung Univ., Tainan, Taiwan
Volume
3
fYear
2001
fDate
2001
Firstpage
529
Abstract
The standard back-propagation (BP) algorithm for multilayer feedforward neural networks is basically a gradient-descent method, it has the problems of local minima and slow convergence. In this paper, a simple method based on the BP algorithm by employing an adaptive learning rate and momentum factor to reduce the training time is presented. Simulation results indicate a superior convergence speed as compared to other competing methods
Keywords
backpropagation; convergence; feedforward neural nets; gradient methods; multilayer perceptrons; BP algorithm; MFNN; adaptive learning rate; back-propagation training; backpropagation training; convergence speed; gradient-descent method; local minima; momentum factor; multilayer feed-forward neural networks; multilayer feedforward neural networks; slow convergence; training time reduction; Acceleration; Convergence; Error correction; Feedforward neural networks; Feedforward systems; Jacobian matrices; Multi-layer neural network; Neural networks; Supervised learning;
fLanguage
English
Publisher
ieee
Conference_Titel
Info-tech and Info-net, 2001. Proceedings. ICII 2001 - Beijing. 2001 International Conferences on
Conference_Location
Beijing
Print_ISBN
0-7803-7010-4
Type
conf
DOI
10.1109/ICII.2001.983111
Filename
983111
Link To Document