具概率分布变时滞随机系统鲁棒稳定性

本文研究了一类有非线性时变随机时滞的线性不确定系统的鲁棒稳定性．其中时变随机时滞表征为伯努利随机过程,具有已知的概率分布和变化范围．通过构造新泛函,建立了基于线性矩阵不等式的鲁棒均方指数稳定的充分条件,此条件易于用MATLABH2具箱来验证．本文所获得结果的主要特征是稳定性条件依赖时滞的概率分布和时滞导数的上界．同时也证明了允许时变随机时滞的时滞比之传统的确定性时滞有更大的变化范围,因此我们的条件比确定性时滞更为保守．算例表明了文中所提方法的有效性．     

不确定随机离散分布时滞神经网络的鲁棒稳定性*

采用Its微分公式和不等式分析技巧,研究了一类不确定随机离散分布时滞神经网络的鲁棒稳定性问题。该模型同时考虑了神经网络模型的两种扰动因素,即随机扰动与不确定性扰动。通过构造适当的Lyapunov泛函,以线性矩阵不等式形式给出了系统在均方根意义下的全局鲁棒稳定性判据,能够利用LMI工具箱很容易地进行检验。此外,仿真结果进一步证明了结论的有效性。     

中立型变时滞系统的鲁棒稳定性

考虑不确定中立型变时滞系统的鲁棒稳定性问题. 首先, 引入新的变量来代替系统的不确定性; 然后, 通过构造一般形式的Lyapunov-Krasovskii泛函、使用积分不等式并引入自由矩阵, 得到了基于线性矩阵不等式的系统稳定性判据. 该结论与中立型时滞, 离散时滞及其导数均相关, 具有较小的保守性. 最后, 通过仿真算例说明了所得到的结论在保守性上优于现存的结果以及中立型时滞, 离散时滞及其导数三者之间的关系.     

具有区间时滞的不确定性随机时滞系统的时滞相关鲁棒稳定性

针对具有区间时滞的不确定性随机时滞系统, 进行稳定性分析. 通过考虑变时滞、时滞的上界及它们的差三者之间的关系, 并应用公式和Lyapunov稳定性理论, 在不忽略任何有用项的前提下, 得到改进的具有区间时滞的随机系统的稳定性判据. 数值实例验证了该方法的有效性.     

不确定时滞BAM神经网络的鲁棒稳定性

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。     

区间时滞神经网络的随机稳定性

分析了区间变时滞的随机神经网络的全局渐进稳定性。区间变时滞不仅考虑了时变因素,而且考虑了时滞时变的上界和下界。通过Itô’s 微分公式和构造适当的李雅普罗夫泛函,并且引入自由权值矩阵,以线性矩阵不等式形式给出了该系统在均方意义下的全局渐进稳定的充分性判据。数值算例进一步证明了结论的有效性。     

时滞随机系统的稳定性分析

针对一类时变时滞随机系统,研究其渐近稳定性问题。通过构造李亚普诺夫函数,引用适当的自由权矩阵,利用积分等式、积分不等式,给出系统渐进稳定的时滞相关的充分条件,其结果用线性矩阵不等式形式表示.用数值算例说明方法的有效性。     

不确定时滞系统时滞相关鲁棒镇定

文章研究了线性不确定时滞系统的时滞相关反馈镇定问题。基于系统的状态反馈给出系统可反馈镇定的控制规律。利用线性矩阵不等式(LMI)给出了系统可反馈镇定的充分条件,最后用实例验证了所得结论的正确性。     

一类线性时滞系统鲁棒控制器的设计

针对一类同时存在状态和控制滞后的线性不确定，时滞系统的鲁棒镇定问题进行了研究，其中系统的不确定项参数时变未知但范数有界、且滞后项也是时变的。对此，基于Lyapunov泛函，利用线性矩阵不等式给出了系统可由状态反馈鲁棒镇定的充分条件，并且利用线性矩阵不等式的解构造了使得系统鲁棒稳定的无记忆状态反馈控制律，所得结果是依赖时滞大小的，且与时滞的导数有关，从而相对减弱了控制器设计的保守性，最后通过一个例子说明了方法的有效性。     

具有饱和执行器的不确定时滞系统的鲁棒稳定性

本文研究了一类具有饱和执行器的不确定时滞系统的鲁棒镇定问题，所考虑的系统具有时变未知有界的不确定参数和状态滞后，基于系统的线性矩阵不等式给出了系统可鲁棒镇定的判据以及鲁棒无记忆状态反馈控制规律。     

New robust exponential stability analysis for uncertain neural networks with time-varying delay

In this paper, the global robust exponential stability is considered for a class of neural networks with parametric uncer-tainties and time-varying delay. By using Lyapunov functional method, and by resorting to the new technique for estimating the upper bound of the derivative of the Lyapunov functional, some less conservative exponential stability criteria are derived in terms of linear matrix inequalities (LMIs). Numerical examples are presented to show the effectiveness of the proposed method.     

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．     

Delay-dependent exponential stability for uncertain neutral stochastic neural networks with interval time-varying delay

This paper is mainly concerned with the problem for the robustly exponential stability in mean square moment of uncertain neutral stochastic neural networks with interval time-varying delay. With an appropriate augmented Lyapunov–Krasovskii functional (LKF) formulated, the convex combination method is utilised to estimate the derivative of the LKF. Some new delay-dependent exponential stability criteria for such systems are obtained in terms of linear matrix inequalities, which involve fewer matrix variables and have less conservatism. Finally, two illustrative numerical examples are given to show the effectiveness of our obtained results.     

Impulsive effect on global exponential stability of BAM fuzzy cellular neural networks with time-varying delays

In this article, a class of impulsive bidirectional associative memory (BAM) fuzzy cellular neural networks (FCNNs) with time-varying delays is formulated and investigated. By employing delay differential inequality and M-matrix theory, some sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive BAM FCNNs with time-varying delays are obtained. In particular, a precise estimate of the exponential convergence rate is also provided, which depends on system parameters and impulsive perturbation intention. It is believed that these results are significant and useful for the design and applications of BAM FCNNs. An example is given to show the effectiveness of the results obtained here.     

时滞Hopfield神经网络的随机稳定性分析

T-S模型提供了一种通过模糊集和模糊推理将复杂的非线性系统表示为线性子模型的方法。研究了时滞Hopfield神经网络的随机稳定性（SFVDHNNs）。首先描述了SFVDHNNs模型,然后用Lyapunov方法研究了SFVDHNNs全局均方指数稳定性,通过可以被一些标准的数值分析方法求解的线性矩阵不等式（LMIs）得出了稳定性标准。     

Finite-time stability of neutral-type neural networks with random time-varying delays

This paper is devoted to the finite-time stability analysis of neutral-type neural networks with random time-varying delays. The randomly time-varying delays are characterised by Bernoulli stochastic variable. This result can be extended to analysis and design for neutral-type neural networks with random time-varying delays. On the basis of this paper, we constructed suitable Lyapunov–Krasovskii functional together and established a set of sufficient linear matrix inequalities approach to guarantee the finite-time stability of the system concerned. By employing the Jensen's inequality, free-weighting matrix method and Wirtinger's double integral inequality, the proposed conditions are derived and two numerical examples are addressed for the effectiveness of the developed techniques.     

Global robust stability of interval neural networks with multiple time-varying delays

In this paper, the global robust stability is investigated for interval neural networks with multiple time-varying delays. The neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Without assuming both the boundedness on the activation functions and the differentiability on the time-varying delays, a new sufficient condition is presented to ensure the existence, uniqueness, and global robust stability of equilibria for interval neural networks with multiple time-varying delays based on the Lyapunov–Razumikhin technique as well as matrix inequality analysis. Several previous results are improved and generalized, and an example is given to show the effectiveness of the obtained results.