共查询到19条相似文献,搜索用时 140 毫秒
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针对一类不确定中立型时变时滞Hopfield神经网络的鲁棒稳定性问题, 构造了一个新Lyapunov-Krasovskii泛函, 并结合自由矩阵方法和牛顿—莱布尼茨公式, 得到了新的时滞相关稳定性判据. 该判据考虑了中立型时变时滞Hopfield神经网络中的参数不确定性, 所得结果以线性矩阵不等式(Linear matrix inequality, LMI)的形式给出, 容易验证. 最后, 通过两个数值算例验证了该结果的有效性及可行性. 该判据对丰富与完善中立型神经网络的稳定性理论体系, 具有积极的意义. 相似文献
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研究了一类具有时变时滞和结构不确定性的Hopfield神经网络鲁棒稳定性问题,应用线性矩阵不等式(LMI)方法得到了不确定时变时滞神经网络全局鲁棒稳定的充分条件,特别是在给定网络平衡点允许偏移的情况下,通过验证定理中给出的LMI,可以非常方便地进行神经网络的鲁棒设计.数值仿真表明了方法的有效性. 相似文献
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本文针对一类带有混合时变时滞 (离散和分布时滞)的区间递归神经网络进行了全局鲁棒稳定性研究.与之前的处理方法不同,在本文中通过使用一种新型的增广Lyapunov-Krasovskii泛函,从而得到了一类新颖的关于区间递归神经网络的时滞依赖全局鲁棒稳定性判据.在新的增广泛函中,由于首次使用了带有激活函数的积分项,系统状态和激活函数之间的关系将被更好地表示出来.因此,本文提出的判据具有更小的保守性.同时,在本文提出的判据中,放松了时变时滞变化率必须小于1的限制.仿真结果进一步证明了本文结果的有效性. 相似文献
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利用自由权值矩阵和不等式分析技巧,研究了一类不确定时滞BAM神经网络的鲁棒稳定性问题。通过构造适当的Lyapunov泛函,对于所有允许的不确定性,以线性矩阵不等式形式给出了时滞BAM神经网络的全局鲁棒稳定性判据,该判据能够利用Matlab的LMI工具箱很容易地进行检验。此外,仿真示例进一步证明了判据的有效性。 相似文献
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针对带分布时滞和离散时滞的不确定中立型系统进行稳定性研究. 基于交互式凸组合方法和下界引理, 通过构造恰当的李雅普诺夫泛函, 适当分割时滞区间, 处理一组由凸参数逆加权的正函数线性组合(交互式凸组合), 给出线性矩阵不等式形式的系统鲁棒稳定性判据. 该方法允许离散时滞为变时滞, 增强了系统的鲁棒性能. 数值算例验证了所得结果的有效性和合理性.
相似文献11.
《Neural Networks, IEEE Transactions on》2008,19(11):1942-1955
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In this article, the global exponential robust stability is investigated for Cohen–Grossberg neural network with both time-varying
and distributed delays. The parameter uncertainties are assumed to be time-invariant and bounded, and belong to given compact
sets. Applying the idea of vector Lyapunov function, M-matrix theory and analysis techniques, several sufficient conditions are obtained to ensure the existence, uniqueness, and
global exponential robust stability of the equilibrium point for the neural network. The methodology developed in this article
is shown to be simple and effective for the exponential robust stability analysis of neural networks with time-varying delays
and distributed delays. The results obtained in this article extend and improve a few recently known results and remove some
restrictions on the neural networks. Three examples are given to show the usefulness of the obtained results that are less
restrictive than recently known criteria.
相似文献
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《国际计算机数学杂志》2012,89(10):2188-2201
The article addresses the problem of global robust exponential stability of interval neural networks with time-varying delays. On the basis of linear matrix inequality technique and M-matrix theory, some novel sufficient conditions for the existence, uniqueness, and global robust exponential stability of the equilibrium point for delayed interval neural networks are presented. It is shown that our results improve and generalize some previously published ones. Some numerical examples and simulations are given to show the effectiveness of the obtained results. 相似文献
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Impulsive effects on stability of fuzzy Cohen-Grossberg neural networks with time-varying delays. 总被引:1,自引:0,他引:1
Qiankun Song Jinde Cao 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2007,37(3):733-741
In this correspondence, the impulsive effects on the stability of fuzzy Cohen-Grossberg neural networks (FCGNNs) with time-varying delays are considered. Several sufficient conditions are obtained ensuring global exponential stability of equilibrium point for the neural networks by the idea of vector Lyapunov function, M-matrix theory, and analytic methods. Moreover, the estimation for exponential convergence rate index is proposed. The obtained results not only show that the stability still remains under certain impulsive perturbations for the continuous stable FCGNNs with time-varying delays, but also present an approach to stabilize the unstable FCGNNs with time-varying delays by utilizing impulsive effects. An example with simulations is given to show the effectiveness of the obtained results. 相似文献
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Song Q. Cao J. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2007,37(3):733-741
In this correspondence, the impulsive effects on the stability of fuzzy Cohen-Grossberg neural networks (FCGNNs) with time-varying delays are considered. Several sufficient conditions are obtained ensuring global exponential stability of equilibrium point for the neural networks by the idea of vector Lyapunov function, M-matrix theory, and analytic methods. Moreover, the estimation for exponential convergence rate index is proposed. The obtained results not only show that the stability still remains under certain impulsive perturbations for the continuous stable FCGNNs with time-varying delays, but also present an approach to stabilize the unstable FCGNNs with time-varying delays by utilizing impulsive effects. An example with simulations is given to show the effectiveness of the obtained results 相似文献
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R. Sriraman 《International journal of systems science》2013,44(9):1742-1756
This paper investigates the global asymptotic stability problem for a class of neutral-type complex-valued neural networks with random time-varying delays. By introducing a stochastic variable with Bernoulli distribution, the information of time-varying delay is assumed to be random time-varying delays. By constructing an appropriate Lyapunov–Krasovskii functional and employing inequality technique, several sufficient conditions are obtained to ensure the global asymptotically stability of equilibrium point for the considered neural networks. The obtained stability criterion is expressed in terms of complex-valued linear matrix inequalities, which can be simply solved by effective YALMIP toolbox in MATLAB. Finally, three numerical examples are given to demonstrate the efficiency of the proposed main results. 相似文献
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Improved conditions for global exponential stability of recurrent neural networks with time-varying delays 总被引:3,自引:0,他引:3
Zhigang Zeng Jun Wang 《Neural Networks, IEEE Transactions on》2006,17(3):623-635
This paper presents new theoretical results on global exponential stability of recurrent neural networks with bounded activation functions and time-varying delays. The stability conditions depend on external inputs, connection weights, and time delays of recurrent neural networks. Using these results, the global exponential stability of recurrent neural networks can be derived, and the estimated location of the equilibrium point can be obtained. As typical representatives, the Hopfield neural network (HNN) and the cellular neural network (CNN) are examined in detail. 相似文献
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带有时变时滞和同步切换的忆阻神经网络的全局指数稳定性 总被引:1,自引:0,他引:1
本文建立并研究了一类具有时变时滞和不同切换机制的忆阻神经网络.利用李雅普诺夫稳定性理论,得到了该神经网络平衡点一致稳定性的充分条件,该充分条件直接有效地反映了时变时滞对稳定性的影响.数值模拟结果验证了理论结果的有效性. 相似文献
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Yu ZhangAuthor Vitae 《Neurocomputing》2011,74(17):3268-3276
In this paper, the robust exponential stability of uncertain impulsive neural networks with time-varying delays and delayed impulses is considered. It is assumed that the considered impulsive neural networks have norm-bounded parametric uncertainties and time-varying delays and the state variables on the impulses may relate to the time-varying delays. By using Lyapunov functions together with Razumikhin technique or with differential inequalities, some new robust exponential stability criteria are provided. Some examples and their simulations, including examples that the stability of which can not be tackled by the existing results, are also presented to illustrate the effectiveness and the advantage of the obtained results. 相似文献