时滞竞争神经网络的全局指数稳定性

对于不同时标的时变时滞竞争神经网络的网络模型,通过构造适当的Lyapunov函数,结合微分不等式分析,研究了时变时滞竞争神经网络的全局指数稳定性,获得了新的全局指数稳定性判据,所得判据推广和改进了前人的相关结论。最后的数值仿真例子证明了该算法的有效性。     

具变时延细胞神经网络模型的全局指数稳定性

通过构造新的Lyapunov泛函．巧妙引入可调实参数，并结合Hardy不等式以及推广的Halanay时延微分不等式，讨论了一类具有可变时延细胞神经网（DCNNs）的全局指数稳定性问题，所得结果改进、推广了文[7]、文[8]中相应的结论，并可应用于以前所不能处理的若干情形。特别地，还通过实例说明了相应准则的应用，扩大了神经网络设计的范围，这在理论上和应用中都有着重要意义。     

一类Markov跳变神经网络的时滞相关鲁棒稳定性

针对一类Markov跳变神经网络,研究了其在系统参数不确定情况下的全局鲁棒稳定性。利用Leibniz Newton公式对原系统进行等价变换,基于Lyapunov 稳定性理论,并结合Moon不等式得到了此类Markov跳变神经网络时滞相关均方鲁棒稳定性的判别条件。所得结果以线性矩阵不等式(linear matrix inequality, LMI)的形式给出,容易被Matlab中的LMI工具箱验证。最后,通过一个算例验证了所得结论的有效性。     

一类时滞动力网络的时滞相关稳定性

研究了节点含时滞的复杂动力网络在平衡点的稳定性.基于时滞微分不等式和李雅普诺夫稳定性理论,提出了此网络的时滞相关的局部渐近稳定的充分条件,并给出了时滞上界的具体表达式.最后的数值模拟分析表明结果是可行且有效的.     

时滞随机线性大系统的指数稳定性

针对一般随机线性时滞微分方程，给出了方程的平凡解的几乎必然指数稳定性的一个充分条件，由此利用时滞随机系统的比较原理建立一般时滞随机线性大系统的二阶矩指数稳定与几乎必然指数稳定新的代数判据．利用恰当的Lyapunov函数结合不等式技巧得到了这些条件．特别是用一个代数方程给出了依赖时滞的Lyapunov指数的估计．并用实例加以验证．     

一类延时细胞神经网络的指数周期性与稳定性

细胞神经网络动态行为的研究是细胞神经网络应用的理论基础。对一类具分布延时细胞神经网络,研究了其全局指数周期性与稳定性。在输出函数满足全局Lipschitz连续的条件下,通过构造合适的Lyapunov泛函,给出了延时细胞神经网络全局指数周期性与稳定性的容易验证的充分条件。给出了算例及其仿真结果来验证所得结论,并说明所得结论与文献[16]的结论是相互独立的。     

时滞离散广义大系统的稳定性分析

从时滞离散广义大系统的满足容许条件的孤立子系统出发,利用李雅普诺夫方法,通过对关联矩阵、输入矩阵和非线性项加上范数有界约束条件,分别研究了时滞离散广义大系统的线性情形和非线性情形的稳定性问题。给出了时滞离散广义大系统的线性情形和非线性情形的稳定性判据,并且得到了关联稳定参数域。最后用数值例子说明所得稳定性判据的实用性和有效性。     

时滞细胞神经网络的全局渐近鲁棒稳定性分析

针对一类具有时滞的细胞神经网络系统,利用新的Lyapunov Krasovskii函数,给出了系统全局渐近鲁棒稳定的时滞相关稳定性条件。其结果以线性矩阵不等式的形式给出,可以很容易求出系统稳定的时滞上界。进而可以很容易得到时滞无关稳定性结果,且该结果包含了现有文献中许多关于时滞无关稳定性分析的结果。数值算例说明了结论的有效性。     

具分布延时细胞神经网络的指数周期与稳定性

研究了具分布延时细胞神经网络的全局指数周期性与指数稳定性。在没有假设激活函数是有界的、可微的、单调增的情况下,通过应用一些新的分析技巧与Halanay-type不等式方法,得到了确保延时细胞神经网络周期解存在唯一且全局指数周期与全局指数稳定的简单的、容易验证的、新的充分条件。并给出了算例及其仿真结果支持所得结论。     

具有时滞的二阶Hopfield神经网络的稳定性分析

对具有时滞的二阶Hopfield型神经网络平衡点的全局渐近稳定性问题进行了研究。在不要求连接权矩阵的对称性和输入输出函数的可微性与单调性的情况下 ,通过构造适当的Lyapunov泛函得到了网络平衡点的存在性和全局渐近稳定性的若干充分条件 ,这些条件可用于设计全局渐近稳定的二阶人工神经网络。     

On the exponential stability of neural networks systems with time-varying delay

In this paper, exponential stability of Hopfield-type neural networks with time-varying delays are analyzed. By using the Lyapunov functional method, sufficient conditions are obtained for general exponential stabilities. At the same time, the output functions do not satisfy the Lipschitz conditions and do not require them to be differential or strictly monotonously increasing. Moreover, all results are established without assuming any symmetry of the connection matrix.A numeric example is pressented to show the effective of these criteria.     

Stability analysis of cellular neural networks with time-varying delay

The global asymptotic stability of cellular neural networks with delays is investigated. Three kinds of time delays have been considered. New delay-dependent stability criteria are proposed and are formulated as the feasibility of some linear matrix inequalities, which can be checked easily by resorting to the recently developed interior-point algorithms. Based on the Finsler Lemma, it is theoretically proved that the proposed stability criteria are less conservative than some existing results.     

Global exponential stability for delayed cellular neural networks and estimate of exponential convergence rate

Some sufficient conditions for the global exponential stability and lower bounds on the rate of exponential convergence of the cellular neural networks with delay (DCNNs) are obtained by means of a method based on delay differential inequality. The method, which does not make use of any Lyapunov functional, is simple and valid for the stability analysis of neural networks with delay. Some previously established results in this paper are shown to be special casses of the presented result.     

Delay-dependent exponential stability of impulsive stochastic systems with time-varying delay

The problem of delay-dependent exponential stability is investigated for impulsive stochastic systems with time-varying delay.Although the exponential stability of impulsive stochastic delay systems has been discussed by several authors,few works have been done on delay-dependent exponential stability of impulsive stochastic delay systems.Firstly,the Lyapunov-Krasovskii functional method combing the free-weighting matrix approach is applied to investigate this problem.Some delay-dependent mean square exponential stability criteria are derived in terms of linear matrix inequalities.In particular,the estimate of the exponential convergence rate is also provided,which depends on system parameters and impulsive effects.The obtained results show that the system will stable if the impulses’ frequency and amplitude are suitably related to the increase or decrease of the continuous flows,and impulses may be used as controllers to stabilize the underlying stochastic system.Numerical examples are given to show the effectiveness of the results.     

Passivity analysis for uncertain stochastic neural networks with discrete interval and distributed time-varying delays

The problem of passivity analysis is investigated for uncertain stochastic neural networks with discrete interval and distributed time-varying delays. The parameter uncertainties are assumed to be norm bounded and the delay is assumed to be time-varying and belongs to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. By constructing proper Lyapunov-Krasovskii functional and employing a combination of the free-weighting matrix method and stochastic analysis technique, new delay dependent passivity conditions are derived in terms of linear matrix inequalities (LMIs). Finally, numerical examples are given to show the less conservatism of the proposed conditions.     

Exponential stability for cellular neural networks: an LMI approach

A new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks （DCNNs） is presented. It is shown that the use of a more general type of Lyapunov-Krasovskii function enables the derivation of new results for an exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.     

New results on the robust stability analysis of neural networks with discrete and distributed time delays

Delay-dependent robust stability of cellular neural networks with time-varying discrete and distributed time-varying delays is considered. Based on Lyapunov stability theory and the linear matrix inequality （LMIs） technique, delay-dependent stability criteria are derived in terms of LMIs avoiding bounding certain cross terms, which often leads to conservatism. The effectiveness of the proposed stability criteria and the improvement over the existing results are illustrated in the numerical examples.