离散分数阶神经网络的全局Mittag-Leffler稳定性

研究了一类离散分数阶神经网络的Mittag-Leffler稳定性问题.首先, 基于离散分数阶微积分理论、神经网络理论,提出了一类离散分数阶神经网络.其次,利用不等式技巧和离散Laplace变换,通过构造合适的Lyapunov函数,得到了离散分数阶神经网络全局Mittag-Leffler稳定的充分性判据.最后,通过一个数值仿真算例验证了所提出理论的有效性.     

具有时滞的细胞神经网络的全局指数稳定性

利用Liapunov函数方法,结合积分不等式技巧,分析了时滞细胞神经网络的平衡点存在的唯一性和全局指数稳定性,保证时滞细胞神经网络全局指数稳定的一个新的充分判据被得到.所得判据比已有文献具有更少的限制,为实际应用提供了方便.     

时滞细胞神经网络的稳定性研究

本文通过构造Lyapunov函数和利用不等式分析技巧，研究了具有时滞的细胞神经网络的稳定性，给出了与时滞无关的网络渐近稳定的充分判据，该判据可用于时滞细胞神经网络的设计与检验，有重要的理论意义与应用价值。     

具有混合时滞的区间神经网络的全局指数稳定性

本文研究了一类具有混合时滞的区间神经网络系统的全局指数稳定性.通过选择适当的Lyapunov泛函,运用不等式技巧,得到了用线性矩阵不等式表示的有关的区间神经网络全局指数稳定的充分判据.通过一个数值实例验证了判据的有效性.     

变时滞细胞神经网络稳定性分析

利用矩阵测度、Liapunov函数和Halanay时滞不等式的方法研究了具有变时滞的细胞神经网络模型平衡点的全局指数稳定性问题.给出了判定平衡点全局指数稳定性的几个代数判据,可用于时滞细胞神经网络的设计与检验,数值算例说明其结果的优越性.     

具有混合时滞的脉冲复值神经网络的全局μ-稳定性

研究了具有离散变化时滞和无界分布时滞的脉冲复值神经网络的稳定性,在所研究的神经网络中,活动函数仅仅要求满足Lipschitz条件.运用同胚映射原理,证明了具有混合时滞的脉冲复值神经网络平衡点的存在性和唯一性.通过构造Lyapunov-Krasovskii泛函,使用自由权矩阵方法和不等式技巧,获得了网络平衡点的全局μ-稳定性的充分性判据.数值仿真实例验证了结果的有效性.     

一类分数阶神经网络模型的稳定性与Hopf分支分析

分析了一类分数阶神经网络的稳定性与Hopf分支问题.基于分数阶稳定性判据,得到了分数阶神经网络模型局部渐近稳定的条件.并以q为分支参数,得到了分数阶系统产生Hopf的条件.最后数值仿真证明了我们的结论.     

时滞神经网络全局渐近稳定性条件

利用Liapunov泛函方法,结合矩阵不等式技巧,分析了时滞细胞神经网络(DCNNs)的平衡点存在的唯一性和全局渐近稳定性,保证DCNNs的全局稳定性的一个新的充分判据被得到．所得判据提供了一些参数来适当地弥补了反馈矩阵与时滞反馈矩阵之间所需要的平衡关系．这些判据可以容易被使用来设计和检验全局稳定的网络．此外,所得判据是与时滞参数无关,且比已有文献具有更少的限制．     

细胞神经网络的指数稳定性

该文研究了具有可变时滞的随机细胞神经网络的指数 稳定性,应用Razumikhin定理与Lyapunov函数,建立了这种细胞神经网络均方指数稳定与几乎必然指数稳定的两类判据,一类是时滞无关而另一类是时滞相关.     

多时滞随机神经网络的稳定性

通过构建李雅普偌夫函数的方法和利用半鞅收敛定理对一类随机时滞神经网络的全局指数稳定进行了分析,提出了易于判定随机时滞神经网络几乎必然指数稳定性新的代数判据,推广了[1]中的主要结论.     

Novel global stability criteria for high-order Hopfield-type neural networks with time-varying delays

This paper discusses a generalized model of high-order Hopfield-type neural networks with time-varying delays. Some novel global stability criteria of the system is derived by using Lyapunov method, linear matrix inequality (LMI) and analytic technique. The LMI-based criteria obtained here are computationally more flexible and more generic than many other existing criteria. A numerical example is given to illustrate our result.     

Dynamic analysis of Markovian jumping impulsive stochastic Cohen–Grossberg neural networks with discrete interval and distributed time-varying delays

In this paper, the dynamic analysis problem is considered for a new class of Markovian jumping impulsive stochastic Cohen–Grossberg neural networks (CGNNs) with discrete interval and distributed delays. The parameter uncertainties are assumed to be norm bounded and the discrete delay is assumed to be time-varying and belonging to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the Lyapunov–Krasovskii functional and stochastic stability theory, delay-interval dependent stability criteria are obtained in terms of linear matrix inequalities. Some asymptotic stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be easily calculated by LMI Toolbox in Matlab. A numerical example is provided to show that the proposed results significantly improve the allowable upper bounds of delays over some existing results in the literature.     

离散神经网络指数稳定的增强李亚普诺夫方法

This paper addresses the problem of robust stability for a class of discrete-time neural networks with time-varying delay and parameter uncertainties.By constructing a new augmented Lyapunov-Krasovskii function,some new improved stability criteria are obtained in forms of linear matrix inequality（LMI） technique.Compared with some recent results in the literature,the conservatism of these new criteria is reduced notably.Two numerical examples are provided to demonstrate the less conservatism and effectiveness of the proposed results.     

Global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type

In this paper, by using the Lyapunov-Krasovskii functional method, we investigate the global robust stability for stochastic interval neural networks with continuously distributed delays of neutral type. Some new stability criteria are presented in terms of linear matrix inequality (LMI). Two numerical examples are also given to show the effectiveness of the obtained results using LMI control toolbox in MATLAB.     

Global robust asymptotic stability analysis of BAM neural networks with time delay and impulse: An LMI approach

The global robust asymptotic stability of bi-directional associative memory (BAM) neural networks with constant or time-varying delays and impulse is studied. An approach combining the Lyapunov-Krasovskii functional with the linear matrix inequality (LMI) is taken to study the problem. Some a criteria for the global robust asymptotic stability, which gives information on the delay-dependent property, are derived. Some illustrative examples are given to demonstrate the effectiveness of the obtained results.     

具有变时滞的离散双向联想记忆神经网络的全局指数稳定性

利用Lyapunov函数和线性矩阵不等式(LMI),给出了判定一类双向联想记忆(BAM)神经网络模型的指数稳定的充分性条件.该条件去掉了以往论文中所要求的激活函数单调,可微分的条件,而且所得结果利用里的工具易于检测.并举例说明本文结果的有效性.     

Stability analysis on delayed neural networks based on an improved delay-partitioning approach

In this paper, the asymptotical stability is investigated for a class of delayed neural networks (DNNs), in which one improved delay-partitioning idea is employed. By choosing an augmented Lyapunov-Krasovskii functional and utilizing general convex combination method, two novel conditions are obtained in terms of linear matrix inequalities (LMIs) and the conservatism can be greatly reduced by thinning the partitioning of delay intervals. Moreover, the LMI-based criteria heavily depend on both the upper and lower bounds on time-delay and its derivative, which is different from the existent ones. Though the results are not presented via standard LMIs, they still can be easily checked by resorting to Matlab LMI Toolbox. Finally, three numerical examples are given to demonstrate that our results can be less conservative than the present ones.     

Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach

Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.     

离散神经网络指数稳定改进条件

研究了一类时滞离散神经网络指数稳定及鲁棒稳定问题.结合线性矩阵不等式技术,构造了一个新的广义李亚普诺夫函数,得到了新的指数稳定条件.数值算例表明与以往文献中的结果相比,新准则具有较弱的保守性.     

Robust stability for stochastic Hopfield neural networks with time delays

In this paper, the asymptotic stability analysis problem is considered for a class of uncertain stochastic neural networks with time delays and parameter uncertainties. The delays are time-invariant, and the uncertainties are norm-bounded that enter into all the network parameters. The aim of this paper is to establish easily verifiable conditions under which the delayed neural network is robustly asymptotically stable in the mean square for all admissible parameter uncertainties. By employing a Lyapunov–Krasovskii functional and conducting the stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the stability criteria. The proposed criteria can be checked readily by using some standard numerical packages, and no tuning of parameters is required. Examples are provided to demonstrate the effectiveness and applicability of the proposed criteria.