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具有泄漏、混合时滞和p-逆Hölder激活函数的马尔可夫跳变随机脉冲不确定双向联想记忆神经网络的全局指数稳定性

Global exponential stability of Markovian jumping stochastic impulsive uncertain BAM neural networks with leakage, mixed time delays, and -inverse Hölder activation functions.

作者信息

Maharajan C, Raja R, Cao Jinde, Ravi G, Rajchakit G

机构信息

1Department of Mathematics, Alagappa University, Karaikudi, India.

2Ramanujan Centre for Higher Mathematics, Alagappa University, Karaikudi, India.

出版信息

Adv Differ Equ. 2018;2018(1):113. doi: 10.1186/s13662-018-1553-7. Epub 2018 Mar 27.

Abstract

This paper concerns the problem of enhanced results on robust finite time passivity for uncertain discrete time Markovian jumping BAM delayed neural networks with leakage delay. By implementing a proper Lyapunov-Krasovskii functional candidate, reciprocally convex combination method, and linear matrix inequality technique, we derive several sufficient conditions for varying the passivity of discrete time BAM neural networks. Further, some sufficient conditions for finite time boundedness and passivity for uncertainties are proposed by employing zero inequalities. Finally, the enhancement of the feasible region of the proposed criteria is shown via numerical examples with simulation to illustrate the applicability and usefulness of the proposed method.

摘要

本文研究了具有泄漏时滞的不确定离散时间马尔可夫跳变BAM时滞神经网络在鲁棒有限时间无源方面的增强结果问题。通过构造合适的Lyapunov-Krasovskii泛函、采用倒数凸组合方法以及线性矩阵不等式技术,我们推导了几个用于改变离散时间BAM神经网络无源特性的充分条件。此外,利用零不等式给出了不确定性的有限时间有界性和无源的一些充分条件。最后,通过数值例子和仿真展示了所提准则可行域的增强,以说明所提方法的适用性和有效性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/590e/5942391/c5b4f3a7d9f0/13662_2018_1553_Fig1_HTML.jpg

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