Robust stability analysis for uncertain stochastic neural networks with mixed time-varying delays

2010 ◽  
Vol 2 (4) ◽  
pp. 364 ◽  
Author(s):  
Yonggang Chen ◽  
Tiheng Qin
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Robust Stability Analysis ◽  
Uncertain Stochastic Neural Networks

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.

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