Multiple-interval-dependent robust stability analysis for uncertain stochastic neural networks with mixed-delays

Complexity ◽  
2014 ◽  
Vol 21 (1) ◽  
pp. 147-162 ◽  
Author(s):  
Jianwei Xia ◽  
Ju H. Park ◽  
Hao Shen
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Mixed Delays ◽  
Robust Stability Analysis ◽  
Uncertain Stochastic Neural Networks

Robust Stability Analysis of Delayed Stochastic Neural Networks via Wirtinger-Based Integral Inequality

2021 ◽  
Vol 33 (1) ◽  
pp. 227-243
Author(s):  
R. Suresh ◽  
A. Manivannan
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Matrix Inequalities ◽  
Parameter Uncertainties ◽  
Robust Stability Analysis ◽  
Uncertain Stochastic Neural Networks ◽  
Delay Dependent ◽  
The Given

We discuss stability analysis for uncertain stochastic neural networks (SNNs) with time delay in this letter. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and utilizing Wirtinger inequalities for estimating the integral inequalities, the delay-dependent stochastic stability conditions are derived in terms of linear matrix inequalities (LMIs). We discuss the parameter uncertainties in terms of norm-bounded conditions in the given interval with constant delay. The derived conditions ensure that the global, asymptotic stability of the states for the proposed SNNs. We verify the effectiveness and applicability of the proposed criteria with numerical examples.

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