DocumentCode
2561951
Title
Novel global robust exponential stability criteria for Cohen-Grossberg neural networks with time-varying delays
Author
Yuan, Yufa ; Li, Xiaolin ; Zheng, Yufan
Author_Institution
Dept. of Math., Shanghai Univ., Shanghai
fYear
2008
fDate
2-4 July 2008
Firstpage
2494
Lastpage
2499
Abstract
In this paper, several novel sufficient criteria are derived for checking the uniqueness and global robust exponential stability of the equilibrium point for interval Cohen-Grossberg neural networks with time-varying delays. A new approach combing the Lyapunov functional with the matrix inequality techniques is taken to investigate this problem. Also, some remarks and two examples are given to show the effectiveness of the proposed results.
Keywords
Lyapunov methods; asymptotic stability; delays; matrix algebra; neural nets; Lyapunov functional; global robust exponential stability criteria; matrix inequality techniques; neural networks; time-varying delays; Convergence; Delay systems; Electronic mail; Linear matrix inequalities; Mathematics; Neural networks; Robust stability; Signal processing; Stability criteria; Symmetric matrices; Interval Cohen-Grossberg Neural Networks; Lyapunov Functional; Robust Exponential Stability; Time-varying Delays;
fLanguage
English
Publisher
ieee
Conference_Titel
Control and Decision Conference, 2008. CCDC 2008. Chinese
Conference_Location
Yantai, Shandong
Print_ISBN
978-1-4244-1733-9
Electronic_ISBN
978-1-4244-1734-6
Type
conf
DOI
10.1109/CCDC.2008.4597774
Filename
4597774
Link To Document