scholarly journals New Results on Passivity Analysis of Stochastic Neural Networks with Time-Varying Delay and Leakage Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-18
Author(s):  
YaJun Li ◽  
Zhaowen Huang
Keyword(s):  
Neural Networks ◽  
Leakage Delay ◽  
Stochastic Neural Networks ◽  
Time Varying ◽  
Time Varying Delay ◽  
Weighting Matrix ◽  
Analysis Theory ◽  
Delay Dependent ◽  
The Impact ◽  
Varying Delay

The passivity problem for a class of stochastic neural networks systems (SNNs) with varying delay and leakage delay has been further studied in this paper. By constructing a more effective Lyapunov functional, employing the free-weighting matrix approach, and combining with integral inequality technic and stochastic analysis theory, the delay-dependent conditions have been proposed such that SNNs are asymptotically stable with guaranteed performance. The time-varying delay is divided into several subintervals and two adjustable parameters are introduced; more information about time delay is utilised and less conservative results have been obtained. Examples are provided to illustrate the less conservatism of the proposed method and simulations are given to show the impact of leakage delay on stability of SNNs.

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.

scholarly journals Stochastic Passivity of Uncertain Neural Networks with Time-Varying Delays

2009 ◽  
Vol 2009 ◽  
pp. 1-16 ◽  
Author(s):  
Jianting Zhou ◽  
Qiankun Song ◽  
Jianxi Yang
Keyword(s):  
Neural Networks ◽  
Linear Matrix ◽  
Time Varying ◽  
Matrix Inequalities ◽  
Time Varying Delay ◽  
Weighting Matrix ◽  
Analysis Technique ◽  
Uncertain Neural Networks ◽  
Delay Dependent ◽  
Varying Delay

The passivity problem is investigated for a class of stochastic uncertain neural networks with time-varying delay as well as generalized activation functions. By constructing appropriate Lyapunov-Krasovskii functionals, and employing Newton-Leibniz formulation, the free-weighting matrix method, and stochastic analysis technique, a delay-dependent criterion for checking the passivity of the addressed neural networks is established in terms of linear matrix inequalities (LMIs), which can be checked numerically using the effective LMI toolbox in MATLAB. An example with simulation is given to show the effectiveness and less conservatism of the proposed criterion. It is noteworthy that the traditional assumptions on the differentiability of the time-varying delays and the boundedness of its derivative are removed.

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