DocumentCode
768247
Title
A recurrent Newton algorithm and its convergence properties
Author
Kuan, Chung-Ming
Author_Institution
Dept. of Econ., Nat. Taiwan Univ., Taipei, Taiwan
Volume
6
Issue
3
fYear
1995
fDate
5/1/1995 12:00:00 AM
Firstpage
779
Lastpage
782
Abstract
In this paper a recurrent Newton algorithm for an important class of recurrent neural networks is introduced. It is noted that a suitable constraint must be imposed on recurrent variables to ensure proper convergence behavior. The simulation results show that the proposed Newton algorithm with the suggested constraint performs uniformly better than the backpropagation algorithm and the Newton algorithm without the constraint, in terms of mean-squared errors
Keywords
Newton method; convergence of numerical methods; learning (artificial intelligence); recurrent neural nets; convergence properties; mean-squared errors; recurrent Newton algorithm; recurrent neural networks; Backpropagation algorithms; Convergence; Feedforward neural networks; Neural networks; Output feedback; Process control; Recurrent neural networks; Signal processing algorithms; System identification; Target recognition;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/72.377987
Filename
377987
Link To Document