scholarly journals Delay-probability-distribution-dependent robust stability analysis for stochastic neural networks with time-varying delay

2009 ◽  
Vol 19 (10) ◽  
pp. 1333-1340 ◽  
Author(s):  
Jie Fu ◽  
Huaguang Zhang ◽  
Tiedong Ma
Keyword(s):  
Neural Networks ◽  
Stability Analysis ◽  
Probability Distribution ◽  
Robust Stability ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Time Varying Delay ◽  
Robust Stability 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.

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