Stability analysis for discrete-time Markovian jump neural networks with mixed time-delays

2012 ◽  
Vol 39 (6) ◽  
pp. 6174-6181 ◽  
Author(s):  
Zheng-Guang Wu ◽  
Peng Shi ◽  
Hongye Su ◽  
Jian Chu
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Discrete Time ◽  
Time Delays ◽  
Markovian Jump ◽  
Mixed Time Delays ◽  
Markovian Jump Neural Networks

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.

Stability Analysis for Discrete-Time Stochastic Neural Networks with Mixed Time Delays via Delay-Paritioning

2011 ◽  
Vol 228-229 ◽  
pp. 464-470
Author(s):  
Xia Zhou ◽  
Shou Ming Zhong
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Discrete Time ◽  
Time Delays ◽  
Optimization Problems ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Parameter Uncertainties ◽  
Mixed Time Delays

This paper revisits the problem of stability analysis for discrete-time stochastic neural networks (DSNNs) with mixed time-varying delays in the state. 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. A new delay-dependent stability criterion is presented by constructing a novel Lyapunov-Krasovskii functional and utilizing the delay partitioning idea and free-weighting matrix approach, Which is less conservative than the existing ones. This criterion can be developed in the frame of convex optimization problems and then solved via standard numerical software. These conditions are formulated in the forms of linear matrix inequalities, which feasibility can be easily checked by using Matlab LMI Toolbox.

Sign in / Sign up

Export Citation Format

Share Document