New passivity results for uncertain discrete-time stochastic neural networks with mixed time delays

2010 ◽  
Vol 73 (16-18) ◽  
pp. 3291-3299 ◽  
Author(s):  
Hongyi Li ◽  
Chuan Wang ◽  
Peng Shi ◽  
Huijun Gao
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Time Delays ◽  
Stochastic Neural Networks ◽  
Mixed Time Delays

Stability analysis for discrete-time stochastic neural networks with mixed time delays and impulsive effects

2010 ◽  
Vol 88 (12) ◽  
pp. 885-898 ◽  
Author(s):  
R. Raja ◽  
R. Sakthivel ◽  
S. Marshal Anthoni
Keyword(s):  
Neural Networks ◽  
Equilibrium Point ◽  
Discrete Time ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Impulsive Effects ◽  
Linear Matrix ◽  
Stochastic Neural Networks ◽  
Mixed Time Delays ◽  
The Stability

This paper investigates the stability issues for a class of discrete-time stochastic neural networks with mixed time delays and impulsive effects. By constructing a new Lyapunov–Krasovskii functional and combining with the linear matrix inequality (LMI) approach, a novel set of sufficient conditions are derived to ensure the global asymptotic stability of the equilibrium point for the addressed discrete-time neural networks. Then the result is extended to address the problem of robust stability of uncertain discrete-time stochastic neural networks with impulsive effects. One important feature in this paper is that the stability of the equilibrium point is proved under mild conditions on the activation functions, and it is not required to be differentiable or strictly monotonic. In addition, two numerical examples are provided to show the effectiveness of the proposed method, while being less conservative.

$$H_{\infty }$$ H ∞ filtering for discrete-time fuzzy stochastic neural networks with mixed time-delays

2015 ◽  
Vol 52 (1-2) ◽  
pp. 1-26 ◽  
Author(s):  
Yajun Li ◽  
Wenping Xiao ◽  
Jingzhao Li ◽  
Like Jiao
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Time Delays ◽  
Stochastic Neural Networks ◽  
Mixed Time Delays

On passivity and robust passivity for discrete-time stochastic neural networks with randomly occurring mixed time delays

2017 ◽  
Vol 31 (1) ◽  
pp. 65-78 ◽  
Author(s):  
Jiahui Li ◽  
Hongli Dong ◽  
Zidong Wang ◽  
Nan Hou ◽  
Fuad E. Alsaadi
Keyword(s):  
Neural Networks ◽  
Discrete Time ◽  
Time Delays ◽  
Stochastic Neural Networks ◽  
Mixed Time Delays

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.

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