DocumentCode
2389760
Title
A stable learning algorithm for recurrent neural networks
Author
Parthasarathy, Guturu ; Pareek, Harish ; Ananthraj, P.
Author_Institution
Dept. of Electron. & Electr. Commun. Eng., Indian Inst. of Technol., Kharagpur, India
fYear
1991
fDate
10-13 Nov 1991
Firstpage
186
Lastpage
191
Abstract
The authors used the Liapunov approach to derive a new set of sufficient conditions that explain the stability of feedforward networks. A simplification of these conditions results in a new recurrent backpropagation algorithm. This algorithm preserves the local updating characteristic of the original algorithm but is, at the same time, found to be quite effective even for problems which offered resistance to solution by L. B. Almeida´s (1987) approach
Keywords
Lyapunov methods; learning systems; neural nets; stability; Liapunov approach; feedforward networks; recurrent backpropagation algorithm; recurrent neural networks; stable learning algorithm; Annealing; Artificial intelligence; Backpropagation algorithms; Iterative algorithms; Multi-layer neural network; Multilayer perceptrons; Neural networks; Pattern classification; Recurrent neural networks; Stability criteria;
fLanguage
English
Publisher
ieee
Conference_Titel
Tools for Artificial Intelligence, 1991. TAI '91., Third International Conference on
Conference_Location
San Jose, CA
Print_ISBN
0-8186-2300-4
Type
conf
DOI
10.1109/TAI.1991.167094
Filename
167094
Link To Document