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．     

不确定变时滞系统的一个时滞相关稳定性判据

本文讨论不确定变时滞系统的稳定性问题.基于一个新的Lyapunov泛函,并利用一种新的方法处理不确定项,得到了不确定变时滞系统的一个时滞相关的稳定性判据,并利用矩阵不等式的形式给出该判据.与已有文献相比较,所得结论允许时滞导数(?)(t)(?)1且具有较少的限制条件,因此具有较弱的保守性.最后,通过两个例子验证了所给结论的正确性.     

具有脉冲效应的灰色随机时滞系统的鲁棒稳定性

研究了一类具有脉冲效应和时变时滞的灰色随机系统的鲁棒稳定性问题。在给出了脉冲随机泛函微分系统随机稳定性的条件的基础上,首先利用Lyapunov-KrasoVskii泛函法和灰矩阵的连续矩阵覆盖的分解技术,得到了具有脉冲效应和时变时滞的灰色随机系统的随机鲁棒稳定性判据,进而基于所得的这个随机鲁棒稳定性判据和Dini导数,给出了该系统指数鲁棒稳定性的判据。实例表明,所得判据是有效的和实用的。     

时滞Lurie系统的绝对稳定性准则

本文用频率法研究Luire系统的绝对稳定性，得到了系统在小时滞和全时滞情形的绝对稳定性判据．     

多时滞退化微分系统指数稳定的代数判据

本文研究退化多时滞微分系统的指数稳定性,得到了退化多时滞微分系统指数稳定性的代数判据,给出的一个例子说明所得结果的应用,同时给出了中立型退化时滞微分系统指数稳定的判定方法.     

变时滞Hopfield神经网络的周期解存在性及其全局指数稳定性

利用拓扑度理论中的连续性引理和推广Halanay不等式研究了变时滞的细胞神经网络的周期解的存在性及全局指数稳定性.给出了判别周期解及指数稳定性的代数判据,所得判据易于检验,具有广泛的实用性．同时,改进了已有文献的相关结论,最后通过数值例子说明结论的有效性．     

时滞微分方程的稳定性

该文利用时滞Gronwall Bellman不等式得到了一些判定时滞微分方程稳定性的充分条件, 特别地,为方便应用,对线性时滞微分方程给出了一些仅与方程右端项有关的简明判据.     

一类含时变分布时滞的退化中立型系统的鲁棒稳定性(英文)

考虑含分布时滞的退化中立型系统的鲁棒稳定性.利用算子Ω的稳定性和线性矩阵不等式得到一个新的鲁棒稳定性判据,本判据将中立型时滞、时变离散时滞、时变分布时滞和退化中立型系统一起考虑,相比已有文献具有较低的保守性.利用Matlab可以验证本判据的有效性.     

随机中立型泛函微分方程的稳定性态

给出了一类中立型随机泛函方程的随机一致稳定性的充分条件,利用了新的分析技巧处理中立型时滞项,得到了中立型随机时滞泛函微分方程渐近稳定性的充分判据.在处理各种渐近估计是有效的.     

一般时间尺度上时滞脉冲系统的双测度稳定性

讨论了一般时间尺度上时滞脉冲系统的双测度稳定性问题.我们引进了一个新的概念即一般时间尺度上时滞脉冲系统的(h_0,h)稳定性.利用Lyapunov函数法和分析法得到了一般时间尺度上时滞脉冲系统解的双测度稳定性判据.最后给出了一个例子以说明本文结论的有效性.     

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.     

Robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay

The purpose of this paper is to investigate the robust exponential stability of discrete‐time uncertain impulsive neural networks with time‐varying delay. By using Lyapunov functions together with Razumikhin technique, some new robust exponential stability criteria are presented. The obtained results show that the robust stability can be retained under certain impulsive perturbations for the neural network, which has the robust stability property. The obtained results also show that impulses can robustly stabilize the neural network, which does not have the robust stability property. Some examples, together with their simulations, are also given to show the effectiveness and the advantage of the presented results. It should be noted that the impulsive robust exponential stabilization result for discrete‐time neural network with time‐varying delay is given for the first time. Copyright © 2012 John Wiley & Sons, Ltd.     

Robust exponential stability and stabilizability of linear parameter dependent systems with delays

The robust exponential stability and stabilizability problems are addressed in this paper for a class of linear parameter dependent systems with interval time-varying and constant delays. In this paper, restrictions on the derivative of the time-varying delay is not required which allows the time-delay to be a fast time-varying function. Based on the Lyapunov-Krasovskii theory, we derive delay-dependent exponential stability and stabilizability conditions in terms of linear matrix inequalities (LMIs) which can be solved by various available algorithms. Numerical examples are given to illustrate the effectiveness of our theoretical results.     

Delay decomposition approach to state estimation of neural networks with mixed time‐varying delays and Markovian jumping parameters

This paper investigates the state estimation of neural networks with mixed time‐varying delays and Markovian jumping parameters. By developing a delay decomposition approach, the information of the delayed plant states can be taken into full consideration. On the basis of the new Lyapunov–Krasovskii functional, some inequality techniques, stochastic stability theory and delay‐dependent stability criteria are obtained in terms of linear matrix inequalities. Finally, three numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results. Copyright © 2012 John Wiley & Sons, Ltd.     

Delay decomposition approach to stability analysis for uncertain fuzzy Hopfield neural networks with time-varying delay

This paper is concerned with delay-dependent stability analysis for uncertain Tagaki–Sugeno (T-S) fuzzy Hopfield neural networks (UFHNNs) with time-varying delay. By decomposing the delay interval into multiple equidistant subintervals, Lyapunov–Krasovskii functionals (LKFs) are constructed on these intervals. Employing these LKFs, a new stability criterion is proposed in terms of Linear Matrix Inequalities (LMIs), which is dependent on the size of the time delay and can be easily verified by MATLAB LMI toolbox. Numerical examples are given to illustrative the effectiveness of the proposed method.     

Exponential stability for markovian jumping stochastic BAM neural networks with mode‐dependent probabilistic time‐varying delays and impulse control

In this article, an exponential stability analysis of Markovian jumping stochastic bidirectional associative memory (BAM) neural networks with mode‐dependent probabilistic time‐varying delays and impulsive control is investigated. By establishment of a stochastic variable with Bernoulli distribution, the information of probabilistic time‐varying delay is considered and transformed into one with deterministic time‐varying delay and stochastic parameters. By fully taking the inherent characteristic of such kind of stochastic BAM neural networks into account, a novel Lyapunov‐Krasovskii functional is constructed with as many as possible positive definite matrices which depends on the system mode and a triple‐integral term is introduced for deriving the delay‐dependent stability conditions. Furthermore, mode‐dependent mean square exponential stability criteria are derived by constructing a new Lyapunov‐Krasovskii functional with modes in the integral terms and using some stochastic analysis techniques. The criteria are formulated in terms of a set of linear matrix inequalities, which can be checked efficiently by use of some standard numerical packages. Finally, numerical examples and its simulations are given to demonstrate the usefulness and effectiveness of the proposed results. © 2014 Wiley Periodicals, Inc. Complexity 20: 39–65, 2015     

A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach

In this paper, global asymptotic stability is discussed for neural networks with time-varying delay. Several new criteria in matrix inequality form are given to ascertain the uniqueness and global asymptotic stability of equilibrium point for neural networks with time-varying delay based on Lyapunov method and Linear Matrix Inequality (LMI) technique. The proposed LMI approach has the advantage of considering the difference of neuronal excitatory and inhibitory efforts, which is also computationally efficient as it can be solved numerically using recently developed interior-point algorithm. In addition, the proposed results generalize and improve previous works. The obtained criteria also combine two existing conditions into one generalized condition in matrix form. An illustrative example is also given to demonstrate the effectiveness of the proposed results.     

A quantitative study on the stability of delay discrete singular systems

For quadratic delay discrete singular systems, an algebraic criterion on the stability is established, and the size of the uniform stability region and asymptotic stability region around zero is estimated. Hence, the criterion is both qualitative and quantitative. With the computer techniques, the criterion dependent of delay is easy test and applies to the application in the practice. An illustrative simulation is given to illustrate the application of the obtained result.     

Robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays

This paper investigates robust stability analysis for Markovian jumping interval neural networks with discrete and distributed time-varying delays. The parameter uncertainties are assumed to be bounded in given compact sets. The delay is assumed to be time-varying and belong to a given interval, which means that the lower and upper bounds of interval time-varying delays are available. Based on the new Lyapunov–Krasovskii functional (LKF), some inequality techniques and stochastic stability theory, new delay-dependent stability criteria have been obtained in terms of linear matrix inequalities (LMIs). Finally, two numerical examples are given to illustrate the less conservative and effectiveness of our theoretical results.     

LMI-based criteria for stability of high-order neural networks with time-varying delay

This paper investigates the stability of a class of high-order neural networks with time-varying delay, which can be considered as an expansion of Hopfield neural networks and is seldom considered in the literature. Based on the Lyapunov stability theory and linear matrix inequality (LMI) technique, sufficient conditions guaranteeing the global exponential stability of the equilibrium point are presented. Two examples are given to show the effectiveness of the proposed conditions. The obtained results are also shown to be different from and more general than existing ones.