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具有连续时变延迟分量的中立型切换Hopfield神经网络的新的依赖延迟间隔的稳定性准则。

New delay-interval-dependent stability criteria for switched Hopfield neural networks of neutral type with successive time-varying delay components.

作者信息

Manivannan R, Samidurai R, Cao Jinde, Alsaedi Ahmed

机构信息

Department of Mathematics, Thiruvalluvar University, Vellore, Tamil Nadu 632 115 India.

Department of Mathematics, and Research Center for Complex Systems and Network Sciences, Southeast University, Nanjing, 210 096 China ; Department of Mathematics, Faculty of Science, King Abdulaziz University, Jeddah, 21589 Saudi Arabia.

出版信息

Cogn Neurodyn. 2016 Dec;10(6):543-562. doi: 10.1007/s11571-016-9396-y. Epub 2016 Jul 19.

Abstract

This paper deals with the problem of delay-interval-dependent stability criteria for switched Hopfield neural networks of neutral type with successive time-varying delay components. A novel Lyapunov-Krasovskii (L-K) functionals with triple integral terms which involves more information on the state vectors of the neural networks and upper bound of the successive time-varying delays is constructed. By using the famous Jensen's inequality, Wirtinger double integral inequality, introducing of some zero equations and using the reciprocal convex combination technique and Finsler's lemma, a novel delay-interval dependent stability criterion is derived in terms of linear matrix inequalities, which can be efficiently solved via standard numerical software. Moreover, it is also assumed that the lower bound of the successive leakage and discrete time-varying delays is not restricted to be zero. In addition, the obtained condition shows potential advantages over the existing ones since no useful term is ignored throughout the estimate of upper bound of the derivative of L-K functional. Using several examples, it is shown that the proposed stabilization theorem is asymptotically stable. Finally, illustrative examples are presented to demonstrate the effectiveness and usefulness of the proposed approach with a four-tank benchmark real-world problem.

摘要

本文研究具有连续时变延迟分量的中立型切换Hopfield神经网络的延迟区间依赖稳定性准则问题。构造了一种具有三重积分项的新型Lyapunov-Krasovskii(L-K)泛函,该泛函包含了更多关于神经网络状态向量和连续时变延迟上界的信息。通过使用著名的Jensen不等式、Wirtinger二重积分不等式,引入一些零等式,并利用倒数凸组合技术和Finsler引理,以线性矩阵不等式的形式导出了一种新型的延迟区间依赖稳定性准则,该准则可通过标准数值软件有效求解。此外,还假设连续泄漏和离散时变延迟的下界不限于零。此外,所得到的条件在现有条件方面显示出潜在优势,因为在估计L-K泛函导数的上界时没有忽略任何有用项。通过几个例子表明,所提出的镇定定理是渐近稳定的。最后,给出了说明性例子,以证明所提出方法对于一个四水箱基准实际问题的有效性和实用性。

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