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
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;
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
DOI :
10.1109/CCDC.2008.4597774
