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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.

DOI:10.1007/s11571-016-9396-y
PMID:27891202
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5106451/
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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本文引用的文献

1
Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays.基于控制丢包和加性时变时滞的复杂动力网络同步的随机采样数据控制。
Neural Netw. 2015 Jun;66:46-63. doi: 10.1016/j.neunet.2015.02.011. Epub 2015 Mar 2.
2
Delay-decomposing approach to robust stability for switched interval networks with state-dependent switching.具有状态相关切换的切换区间网络的鲁棒稳定性的时延分解方法。
Cogn Neurodyn. 2014 Aug;8(4):313-26. doi: 10.1007/s11571-014-9279-z. Epub 2014 Jan 19.
3
Robust exponential stability of uncertain delayed neural networks with stochastic perturbation and impulse effects.具有随机扰动和脉冲效应的不确定时滞神经网络的鲁棒指数稳定性。
IEEE Trans Neural Netw Learn Syst. 2012 Jun;23(6):866-75. doi: 10.1109/TNNLS.2012.2192135.
4
Stochastic sampled-data control for state estimation of time-varying delayed neural networks.时变时滞神经网络状态估计的随机采样数据控制。
Neural Netw. 2013 Oct;46:99-108. doi: 10.1016/j.neunet.2013.05.001. Epub 2013 May 10.
5
New delay-dependent stability criteria for neural networks with two additive time-varying delay components.具有两个相加时变延迟分量的神经网络的新的依赖延迟的稳定性准则。
IEEE Trans Neural Netw. 2011 May;22(5):812-8. doi: 10.1109/TNN.2011.2114366. Epub 2011 Mar 22.
6
Novel delay-dependent robust stability analysis for switched neutral-type neural networks with time-varying delays via SC technique.基于切换控制技术的时变时滞中立型切换神经网络的新型时滞依赖鲁棒稳定性分析
IEEE Trans Syst Man Cybern B Cybern. 2010 Dec;40(6):1480-91. doi: 10.1109/TSMCB.2010.2040274. Epub 2010 Feb 22.
7
Global exponential stability of generalized recurrent neural networks with discrete and distributed delays.具有离散和分布时滞的广义递归神经网络的全局指数稳定性
Neural Netw. 2006 Jun;19(5):667-75. doi: 10.1016/j.neunet.2005.03.015. Epub 2005 Jul 20.
8
Neural networks and physical systems with emergent collective computational abilities.具有涌现集体计算能力的神经网络与物理系统。
Proc Natl Acad Sci U S A. 1982 Apr;79(8):2554-8. doi: 10.1073/pnas.79.8.2554.
9
Neurons with graded response have collective computational properties like those of two-state neurons.具有分级反应的神经元具有与双态神经元类似的集体计算特性。
Proc Natl Acad Sci U S A. 1984 May;81(10):3088-92. doi: 10.1073/pnas.81.10.3088.