Robust stability results for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

2008 ◽  
Vol 372 (32) ◽  
pp. 5290-5298 ◽  
Author(s):  
R. Rakkiyappan ◽  
P. Balasubramaniam ◽  
S. Lakshmanan
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Discrete Interval ◽  
Uncertain Stochastic Neural Networks ◽  
Stability Results

Delay-Range-Dependent Robust Stability for Uncertain Stochastic Neural Networks with Time-Varying Delays

2012 ◽  
pp. 418-432
Author(s):  
Wei Feng ◽  
Haixia Wu
Keyword(s):  
Neural Networks ◽  
Robust Stability ◽  
Stochastic Analysis ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Asymptotically Stable ◽  
Stability Criteria ◽  
Robust Stability Analysis ◽  
Uncertain Stochastic Neural Networks

This paper is concerned with the robust stability analysis problem for uncertain stochastic neural networks with interval time-varying delays. By utilizing a Lyapunov-Krasovskii functional and conducting stochastic analysis, the authors show that the addressed neural networks are globally, robustly, and asymptotically stable if a convex optimization problem is feasible. Some stability criteria are derived for all admissible uncertainties, and these stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Five numerical examples are given to demonstrate the usefulness of the proposed robust stability criteria.

Sign in / Sign up

Export Citation Format

Share Document