DocumentCode
1040810
Title
An Improved Algebraic Criterion for Global Exponential Stability of Recurrent Neural Networks With Time-Varying Delays
Author
Shen, Yi ; Wang, Jun
Author_Institution
Huazhong Univ. of Sci. & Technol., Wuhan
Volume
19
Issue
3
fYear
2008
fDate
3/1/2008 12:00:00 AM
Firstpage
528
Lastpage
531
Abstract
This brief paper presents an M-matrix-based algebraic criterion for the global exponential stability of a class of recurrent neural networks with decreasing time-varying delays. The criterion improves some previous criteria based on M-matrix and is easy to be verified with the connection weights of the recurrent neural networks with decreasing time-varying delays. In addition, the rate of exponential convergence can be estimated via a simple computation based on the criterion herein.
Keywords
asymptotic stability; matrix algebra; recurrent neural nets; M-matrix-based algebraic criterion; exponential convergence; global exponential stability; recurrent neural networks; time-varying delays; $M$ -matrix; Global exponential stability; recurrent neural networks; time-varying delays; Algorithms; Computer Simulation; Humans; Neural Networks (Computer); Nonlinear Dynamics; Pattern Recognition, Automated; Time Factors;
fLanguage
English
Journal_Title
Neural Networks, IEEE Transactions on
Publisher
ieee
ISSN
1045-9227
Type
jour
DOI
10.1109/TNN.2007.911751
Filename
4435137
Link To Document