Robust dissipativity and passivity analysis for discrete-time stochastic neural networks with time-varying delay

Complexity ◽  
2014 ◽  
Vol 21 (3) ◽  
pp. 47-58 ◽  
Author(s):  
G. Nagamani ◽  
S. Ramasamy ◽  
P. Balasubramaniam
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

Delay-Probability-Distribution-Dependent Stability Analysis for Stochastic Neural Networks with Mixed Time-Varying Delays: The Discrete-Time Case

2011 ◽  
Vol 217-218 ◽  
pp. 600-605
Author(s):  
Xia Zhou ◽  
Shou Ming Zhong
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Probability Distribution ◽  
Discrete Time ◽  
Time Delays ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Time Varying Delay ◽  
Mixed Time Delays ◽  
Varying Delay

The problem of delay-probability-distribution-dependent stability analysis for a class of discrete-time stochastic delayed neural networks (DSNNs) with mixed time delays is investigated. Here the mixed time delays are assumed to be discrete and distributed time delays and the uncertainties are assumed to be time varying norm bounded parameter uncertainties. The information of the probability distribution of the time-varying delay is considered and transformed into parameter matrices of the transferred DSNN model, in which the time-varying delay is characterized by introducing a Bernoulli stochastic variable. By constructing a new augmented Lyapunov-Krasovskii functional and introducing some new analysis techniques, a novel delay-probability-distribution-dependent stable criterion for the DSNN to be stable in the mean square sense are derived. These criteria are formulated in the forms of linear matrix inequalities.

scholarly journals Delay-Range-Dependent Global Robust Passivity Analysis of Discrete-Time Uncertain Recurrent Neural Networks with Interval Time-Varying Delay

2009 ◽  
Vol 2009 ◽  
pp. 1-14 ◽  
Author(s):  
Chien-Yu Lu ◽  
Chin-Wen Liao ◽  
Hsun-Heng Tsai
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Recurrent Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Interval Time ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

This paper examines a passivity analysis for a class of discrete-time recurrent neural networks (DRNNs) with norm-bounded time-varying parameter uncertainties and interval time-varying delay. The activation functions are assumed to be globally Lipschitz continuous. Based on an appropriate type of Lyapunov functional, sufficient passivity conditions for the DRNNs are derived in terms of a family of linear matrix inequalities (LMIs). Two numerical examples are given to illustrate the effectiveness and applicability.

On passivity analysis for stochastic neural networks with interval time-varying delay

2010 ◽  
Vol 73 (4-6) ◽  
pp. 795-801 ◽  
Author(s):  
Jie Fu ◽  
Huaguang Zhang ◽  
Tiedong Ma ◽  
Qingling Zhang
Keyword(s):  
Neural Networks ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Interval Time ◽  
Time Varying Delay ◽  
Passivity Analysis ◽  
Varying Delay

Exponential Stability for Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

2015 ◽  
Vol 742 ◽  
pp. 399-403
Author(s):  
Ya Jun Li ◽  
Jing Zhao Li
Keyword(s):  
Neural Networks ◽  
Exponential Stability ◽  
Sufficient Conditions ◽  
Leakage Delay ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Time Varying Delay ◽  
Varying Delay

This paper investigates the exponential stability problem for a class of stochastic neural networks with leakage delay. By employing a suitable Lyapunov functional and stochastic stability theory technic, the sufficient conditions which make the stochastic neural networks system exponential mean square stable are proposed and proved. All results are expressed in terms of linear matrix inequalities (LMIs). Example and simulation are presented to show the effectiveness of the proposed method.

Sign in / Sign up

Export Citation Format

Share Document