New robust exponential stability analysis for uncertain neural networks with time-varying delay

2008 ◽  
Vol 5 (4) ◽  
pp. 395-400 ◽  
Author(s):  
Yong-Gang Chen ◽  
Wei-Ping Bi
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Exponential Stability ◽  
Time Varying ◽  
Robust Exponential Stability ◽  
Time Varying Delay ◽  
Uncertain Neural Networks ◽  
Varying Delay

scholarly journals Robust Exponential Stability of Impulsive Stochastic Neural Networks with Leakage Time-Varying Delay

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chunge Lu ◽  
Linshan Wang
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Lyapunov Functions ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Mean Square ◽  
Robust Exponential Stability ◽  
Time Varying Delay ◽  
The Stability ◽  
Varying Delay

This paper investigates mean-square robust exponential stability of the equilibrium point of stochastic neural networks with leakage time-varying delays and impulsive perturbations. By using Lyapunov functions and Razumikhin techniques, some easy-to-test criteria of the stability are derived. Two examples are provided to illustrate the efficiency of the results.

ROBUST EXPONENTIAL STABILITY OF MARKOVIAN JUMPING NEURAL NETWORKS WITH TIME-VARYING DELAY

2008 ◽  
Vol 18 (03) ◽  
pp. 207-218 ◽  
Author(s):  
MING GAO ◽  
XUYANG LOU ◽  
BAOTONG CUI
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Global Exponential Stability ◽  
Sufficient Condition ◽  
Time Varying ◽  
Markovian Jumping Parameters ◽  
Markovian Jumping ◽  
Robust Exponential Stability ◽  
Time Varying Delay ◽  
Varying Delay

This paper considers the robust stability of a class of neural networks with Markovian jumping parameters and time-varying delay. By employing a new Lyapunov–Krasovskii functional, a sufficient condition for the global exponential stability of the delayed Markovian jumping neural networks is established. The proposed condition is also extended to the uncertain cases, which are shown to be the improvement and extension of the existing ones. Finally, the validity of the results are illustrated by an example.

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