Finite-time stabilization of stochastic neural networks with time-varying delay via impulsive control

In this paper, the finite-time stabilization problem for memristive Cohen-Grossberg neural networks with time-varying delay is discussed. By using the novel fixed point theory of set-valued maps, we establish the existence theorem of equilibrium point. In order to realize the finite-time stabilization, two different kinds of discontinuous state feedback controllers whether including time-varying delay are designed. Based on the extended Filippov framework and two different kinds of methods whether using finite-time stability theory, some novel sufficient conditions and the upper bound of the settling time for finite-time stabilization are proposed. Finally, two numerical examples are given to demonstrate the validity of theoretical results.

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Novel switching design for finite-time stabilization: Applications to memristor-based neural networks with time-varying delay

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.4976939 ◽

2017 ◽

Vol 27 (2) ◽

pp. 023112 ◽

Cited By ~ 1

Author(s):

Zuo-Wei Cai ◽

Jian-Hua Huang ◽

Li-Hong Huang

Keyword(s):

Neural Networks ◽

Finite Time ◽

Time Varying ◽

Time Varying Delay ◽

Switching Design ◽

Finite Time Stabilization ◽

Varying Delay

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Robust delay-distribution-dependent stability of discrete-time stochastic neural networks with time-varying delay

Neurocomputing ◽

10.1016/j.neucom.2008.01.028 ◽

2009 ◽

Vol 72 (4-6) ◽

pp. 1265-1273 ◽

Cited By ~ 74

Author(s):

Yijun Zhang ◽

Dong Yue ◽

Engang Tian

Keyword(s):

Neural Networks ◽

Discrete Time ◽

Stochastic Neural Networks ◽

Time Varying ◽

Delay Distribution ◽

Time Varying Delay ◽

Varying Delay

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

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.742.399 ◽

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.

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