时滞神经网络的全局渐近稳定性分析

研究一类具有时滞的细胞神经网络的稳定性问题,利用Lyapunov—Krasovskii泛函的方法,给出时滞相关的稳定性判据。稳定性判据是以线性矩阵不等式的形式给出,可以很容易得出时滞的上界。在得到时滞相关的稳定性判据的同时也可以得到时滞无关的稳定性判据。数值算例说明其结果的优越性。     

时滞切换不确定神经网络系统的指数稳定性

本文研究了具有无穷时滞切换不确定细胞神经网络(UCNNs)系统任意切换下的指数稳定性.利用同胚映射和M-矩阵理论,得到UCNNs系统平衡点存在性,唯一性和指数稳定性的充分条件;利用Lyapunov泛函方法,研究了时滞切换UCNNs系统任意切换下的鲁棒指数稳定性,并得到确保系统全局指数稳定的充分条件.     

多模式网络化控制系统稳定性分析

网络传输引起的时滞在网络化控制系统中经常出现．针对存在多个运动模式的网络化控制系统,建立了时滞混杂自动机模型,分析了系统平衡点的稳定性,给出了平衡点保持稳定的充分条件在于构造的与离散状态相关的Lyapunov泛函在离散状态的切换时刻是非增的．当时滞混杂自动机中连续性子系统为线性时不变时滞系统时,可以利用线性矩阵不等式方法来寻找系统的公共Lyapunov泛函,并求解使系统平衡点保持稳定的时滞上界．最后,给出了一个例子说明了分析结果．     

具有时变时滞不确定大系统的鲁棒稳定性判据

研究具有时变时滞的不确定大系统的鲁棒稳定性，利用Lyapunov泛函方法．给出该系统鲁棒稳定的充分性判别条件，并结合算例验证了所得结果的有效性和低保守性。     

时滞无关的CNN稳定性分析及保密通信应用

针对存在延时情况下的细胞神经网络系统稳定性判定问题,构造新型Lyapunov-Krasovskii泛函并结合Lyapunov稳定性理论及线性矩阵不等式,得到用于判定时滞细胞神经网络具有唯一平衡点和全局渐近稳定性的一组充分条件。数值仿真实验结果证明,该判定条件具有较好的可验证性和可实现性,且与时滞无关,适用于保密通信。     

离散多时滞系统的时滞相关鲁棒稳定性分析

研究了具有凸多面体不确定性的离散多时滞系统鲁棒稳定性问题．通过引入一种新的二次型的有限和不等式,采用参数依赖型Lyapunov泛函,导出了适用于具有凸多面体不确定性离散时滞系统的一个新的时滞相关稳定性条件．该方法不需要进行系统模型变换即可得到时滞相关的结果,和已有文献相比,所得结果具有更小的保守性．数值例子说明了该方法的有效性．     

离散分段线性时滞正系统的稳定性分析

研究了离散的切换线性时滞正系统的稳定性问题．通过运用切换线性余正（copositive）Lyapunov泛函和共同线性余正（copositive）Lyapunov泛函分别得到了关于平衡点全局渐近稳定性的线性规划（LP）和线性矩阵不等式（LMI）判别法则．     

时滞标准神经网络模型及其应用

提出一种新的神经网络模型---时滞标准神经网络模型(DSNNM),它由线性动力学系统和有界静态时滞非线性算子连接而成.利用不同的Lyapunov泛函和S方法推导出DSNNM全局渐近稳定性和全局指数稳定性的充分条件,这些条件可表示为线性不等式(LMI)形式.大多数时滞(或非时滞)动态神经网络(DANN)稳定性分析或神经网络控制系统都可以转化为DSNNM,以便用统一的方法进行稳定性分析或镇定控制.从DSNNM应用于时滞联想记忆(BAM)神经网络的稳定性分析以及PH中和过程神经控制器的综合实例,可以看出,得到的稳定性判据扩展并改进了以往文献中的稳定性定理,而且可将稳定性分析推广到非线性控制系统的综合.     

随机细胞神经网络平衡点均方指数稳定性分析

主要利用Lyapunov 泛函方法研究带脉冲的随机时滞神经网络平衡点的均方指数稳定性。主要借助于不等式,随机分析理论给出主要结果。最后给出一数值算例证明结果的有效性。     

时延细胞神经网络的全局渐近稳定性

通过构造新的Lyapunov泛函,在Lyapunov泛函中巧妙引入可调的实参数,并结合不等式运用的一些技巧,讨论了时延细胞神经网络的全局渐近稳定性问题,得到了该模型的平衡点全局渐近稳定的一些新的充分条件。所得的结果改进推广了已有文献中相应的一些结论,并且可应用于以前所不能处理的若干情形。理论分析和数学推导表明,全局渐近稳定性的一个简单充分判据与时延是有关的。所得结果突出了时延对于细胞神经网络的全局渐近稳定性的影响,这对于设计带时延的细胞神经网络有着重要的参考价值。此外,通过实例说明了相应结果的应用,这在理论上和应用中都有着重要的意义。     

Effect of leakage delay on the almost periodic solutions of fuzzy cellular neural networks

ABSTRACT

In this paper, fuzzy cellular neural networks with time-varying delays in leakage terms are investigated. With the help of the differential inequality theory and almost periodic function theory, a set of sufficient criteria that guarantee the existence and exponential stability of almost periodic solutions of fuzzy cellular neural networks with time-varying delays in leakage terms are established. Our results are new and complement some previously known ones. Moreover, numerical simulations are carried out to verify our theoretical results.     

New criteria for globally exponential stability of delayed Cohen–Grossberg neural network

This paper is concerned with analysis problem for the global exponential stability of the Cohen–Grossberg neural networks with discrete delays and with distributed delays. We first prove the existence and uniqueness of the equilibrium point under mild conditions, assuming neither differentiability nor strict monotonicity for the activation function. Then, we employ Lyapunov functions to establish some sufficient conditions ensuring global exponential stability of equilibria for the Cohen–Grossberg neural networks with discrete delays and with distributed delays. Our results are not only presented in terms of system parameters and can be easily verified and also less restrictive than previously known criteria. A comparison between our results and the previous results admits that our results establish a new set of stability criteria for delayed neural networks.     

带两个不同时延神经网络的稳定性研究

讨论了带两个不同时延且有两个神经元系统的局部稳定性,得到了判定神经网络稳定性的一些准则,这些准则有的是与时延有关,而有的是与时延无关（这种情形也称为“无害时延”）;研究方法对于带不同时延且多个神经元网络的稳定性的研究有重要的指导意义。     

Variable-time impulses in BAM neural networks with delays

In this paper, the globally exponential stability of BAM neural networks with time delays and impulses has been studied. Different from most existing publications, the case of variable time impulses is dealt with in the present paper, i.e., impulse occurring is not at fixed instants but depends on the states of systems. By using Lyapunov function and inequality technique, some globally exponential stability criteria of BAM neural networks with time delays and variable-time impulses have been established. When the proposed results can also be applied to the case of fixed-time impulses, it provides new stability conditions for the case of fixed-time impulses. Numerical examples are also given to illustrate the effectiveness of our theoretical results.     

Exponential convergence and stability of delayed fuzzy cellular neural networks with time-varying coefficients

In this paper, the dynamic behaviors of fuzzy cellular neural networks (FCNNs) with time-varying coefficients and delays are considered. Some criteria are established to ensure the exponential convergence or exponential stability of such neural networks. The effectiveness of obtained results is illustrated by a numerical example.     

区间时变细胞神经网络的全局鲁棒指数稳定性

研究了一类区间时变扰动、变时滞细胞神经网络的全局鲁棒指数稳定性问题．利用Leibniz-Newton公式对原系统进行模型变换,并分析了变换模型和原始模型的等价性．基于变换模型,运用线性矩阵不等式的方法,通过选择适当的Lyapunov-Krasovskii泛函,推导了该系统全局鲁棒指数稳定的时滞相关的充分条件．通过数值实例将所得结果与前人的结果相比较,表明了本文所提出的稳定判据具有更低的保守性．     

Stability analysis based on partition trajectory approach for switched neural networks with fractional Brown noise disturbance

In this brief, the stability problem based on feedback control for two types of stochastic neural networks driven by fractional Brown noise is considered. One class is the switched neural networks without time delays and the other one is with time delays. A novel analysis method, very different to the usual approach based on the Itô formula and infinitesimal operator, is proposed in this paper. By the idea of splitting time of trajectory and associating with hölder inequality, some criteria are obtained to guarantee the switched neural networks with two types to be stable. In the end, two numerical examples and auxiliary figures are presented to show the feasibility and effectiveness for the proposed results.     

一类变时滞神经网络的全局指数稳定性

在不要求激活函数有界的前提下,利用Lyapunov泛函方法和线性矩阵不等式(LMI)分析技巧,研究了一类变时滞神经网络平衡点的存在性和全局指数稳定性.给出判别网络全局指数稳定性的判据,推广了现有文献中的一些结果.这些判据具有LMI的形式,进而易于验证.仿真例子表明了所得结果的有效性.     

Stability and Existence of Periodic Solutions of Periodic Cellular Neural Networks with Time-Vary Delays

By using the continuation theorem of Mawhins coincidence degree theory and constructing a suitable Lyapunov function, some new sufficient conditions are obtained ensuring existence and global asymptotical stability of periodic solution of cellular neural networks with periodic coefficients and delays, which do not require the activation functions to be differentiable and monotone nondecreasing. A numerical example is given to illustrate that the criteria are feasible. These results are helpful to design globally asymptotically stable and periodic oscillatory cellular neural networks.     

Global robust stability of delayed neural networks with a class of general activation functions

In this paper, the global robust stability is discussed for delayed neural networks with a class of general activation functions. By constructing new Lyapunov functionals, several novel conditions are derived to guarantee the existence, uniqueness and global robust stability of the equilibrium of neural networks with time delays. These conditions do not require the activation functions to be differentiable, bounded or monotonically nondecreasing. The results obtained here are generalizations of some earlier results reported in the literature for neural networks with time delays. In addition, two examples are given to illustrate our proposed results.