基于LMI的时滞细胞神经网络的全局渐近稳定性分析

研究了一类具有时滞的细胞神经网络的稳定性问题,利用Liapunov-Krasovskii泛函的方法,给出了时滞相关的稳定性判据．稳定性判据是以线性矩阵不等式(LMI)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,包含了已有文章中的很多结果．最后,数值算例说明了结果的优越性．     

具有时滞的细胞神经网络周期解与稳定性

本文研究了具有时滞的细胞神经网络周期解存在性和平凡解的稳定性问题 .利用 Lyapunov函数法并结合不等式分析技巧 ,我们首先证明了时滞细胞神经网络的解是有界的 ,然后建立了时滞细胞神经网络的周期解的存在准则 ,最后在时滞细胞神经网络有平衡点时 ,给出了神经网络系统的平衡点指数稳定的充分条件 .其结果推广了文 [7,8]的相应结果 .     

不连续激活函数的时滞神经网络的多稳定性

研究了不连续激活函数的时滞神经网络的多稳定性问题,在所研究的神经网络中,激活函数并不需要是连续的和单调的．给出了判断该神经网络多个平衡点存在及局部指数稳定的充分条件．最后,给出了两个数值仿真例子来验证本文获得结果的有效性和较小的保守性．     

具有无穷时滞的细胞神经网络的全局稳定性分析

对具有无穷时滞的细胞神经网络平衡点的存在性、唯一性和全局渐近稳定性进行了分析．在放弃了激活函数的有界性、单调性和可微性假设的情况下,得到了系统的平衡点的存在性条件．利用向量Liapunov函数法的思想,构造适当的含有变时滞和无穷时滞的微分-积分不等式,通过对微分-积分不等式的稳定性分析,得到了神经网络系统的全局渐近稳定的充分条件．     

具有时滞的细胞神经网络周期解与稳定性

本文研究了具有时滞的细胞神经网络周期解存在性和平凡解的稳定性问题。利用Ｌｙａｐｕｎｏｖ函数法并结合不等式分析技巧,我们首先证明了时滞细胞网络的解是有界的,然后建立了时滞细胞神经物周期解的存在准则,最后在时滞细胞神经网络有平衡点时,给出了神经网络系统的平衡点指数稳定的充分条件。其结果推广了文「７,８」的相应结果。     

时变耦合变时滞非自治神经网络模型的全局同步性分析

利用Lyapunov泛函方法,对一类时变线性耦合神经网络模型的全局同步性进行了研究．在去掉耦合矩阵的对称性、不可约性和扩散耦合限制的基础上,得到了确保耦合时滞神经网络模型全局同步的充分性条件．所得结果仅依赖于系统中的参数,条件易于验证且不必求矩阵的特征值．     

一类具有时变时滞的神经网络的稳定性分析

研究了一类具有时变时滞的神经网络的稳定性,首先将要研究的模型转化为数学系统模型,利用Lyapunov-Krasovskii稳定性理论和线性矩阵不等式技术(LMI方法),精炼和推广了一些已有的结果,所得结果为时变时滞神经网络提供了新的稳定性判定准则.     

Hopfield型时滞神经网络的指数稳定性

研究了Ｈｏｐｆｉｅｌｄ型随机时滞神经网络ｄｘ（ｔ）＝［－Ａｘ（ｔ）＋Ｂσ（ｘ（ｔ一τ））］ｄｔ＋ｆ（ｔ．ｘ（ｔ）,Ｘ（ｔ—τ））ｄｗ（ｔ）的均方指数稳定性与几乎必然指数稳定性．应用Ｌａｙａｐｕｎｏｖ函数与鞅不等式,建立了这种随机时滞神经网络指数稳定的时滞相关的充分条件．文献中某些关于确定性的时滞神经网络ｘ（ｔ）＝－Ａｘ（ｔ）＋Ｂσ（ｘ（ｔ－τ））与神经网络ｘ（ｔ）＝－Ａｘ（ｔ）＋Ｂσ（ｘ（ｔ））的稳定准则是文中的特殊情况．     

无界变时滞神经网络全局稳定性

该文研究了具无界变时滞的时变神经网络的全局稳定性.利用两种不同的分析方法得到了保证这类神经网络全局渐近稳定的一些充分条件.推广和改进了现有文献中常时滞或时滞为零的相应结果.     

变时滞细胞神经网络的指数稳定的一个充分条件

本文我们研究了变时滞细胞神经网络的指数稳定性。利用分析技巧，获得了变时滞细胞神经网络的指数稳定的充分条件，改进了已有文献中的相应结果。     

Global asymptotical synchronization of chaotic neural networks by output feedback impulsive control: An LMI approach

In this paper, impulsive control for master–slave synchronization schemes consisting of identical chaotic neural networks is studied. Impulsive control laws are derived based on linear static output feedback. A sufficient condition for global asymptotic synchronization of master–slave chaotic neural networks via output feedback impulsive control is established, in which synchronization is proven in terms of the synchronization errors between the full state vectors. An LMI-based approach for designing linear static output feedback impulsive control laws to globally asymptotically synchronize chaotic neural networks is discussed. With the help of LMI solvers, linear output feedback impulsive controllers can be easily obtained along with the bounds of the impulsive intervals for global asymptotic synchronization. The method is finally illustrated by numerical simulations.     

Impulsive controller design for exponential synchronization of chaotic neural networks with mixed delays

This paper considers the chaotic synchronization problem of neural networks with time-varying and distributed delays using impulsive control method. By utilizing the stability theory for impulsive functional differential equations, several impulsive control laws are derived to guarantee the exponential synchronization of neural networks with time-varying and distributed delays. It is shown that chaotic synchronization of the networks is heavily dependent on the designed impulsive controllers. Moreover, these conditions are expressed in terms of LMI and can be easily checked by MATLAB LMI toolbox. Finally, a numerical example and its simulation are given to show the effectiveness and advantage of the proposed control schemes.     

Stability of impulsive stochastic differential delay systems and its application to impulsive stochastic neural networks

This paper is concerned with the stability of n-dimensional stochastic differential delay systems with nonlinear impulsive effects. First, the equivalent relation between the solution of the n-dimensional stochastic differential delay system with nonlinear impulsive effects and that of a corresponding n-dimensional stochastic differential delay system without impulsive effects is established. Then, some stability criteria for the n-dimensional stochastic differential delay systems with nonlinear impulsive effects are obtained. Finally, the stability criteria are applied to uncertain impulsive stochastic neural networks with time-varying delay. The results show that, this convenient and efficient method will provide a new approach to study the stability of impulsive stochastic neural networks. Some examples are also discussed to illustrate the effectiveness of our theoretical results.     

Mean square exponential stability in high-order stochastic impulsive neural networks with time-varying delays

In this paper, we consider a class of stochastic impulsive high-order neural networks with time-varying delays. By using Lyapunov functional method, LMI method and mathematics induction, some sufficient conditions are derived for the globally exponential stability of the equilibrium point of the neural networks in mean square. It is believed that these results are significant and useful for the design and applications of impulsive stochastic high-order neural networks.     

Stability in impulsive Cohen-Grossberg-type BAM neural networks with distributed delays

In this paper, a class of impulsive Cohen-Grossberg-type bi-directional associative memory (BAM) neural networks with distributed delays is investigated. By establishing an integro-differential inequality with impulsive initial conditions and employing the homeomorphism theory, the M-matrix theory and inequality technique, some new general sufficient conditions ensuring the existence, uniqueness and global exponential stability of equilibrium point for impulsive Cohen-Grossberg-type BAM neural networks with distributed delays are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on the system parameters and impulsive disturbed intension. An example is given to show the effectiveness of the results obtained here.     

Global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays

In this paper, by means of constructing the extended impulsive delayed Halanay inequality and by Lyapunov functional methods, we analyze the global exponential stability and global attractivity of impulsive Hopfield neural networks with time delays. Some new sufficient conditions ensuring exponential stability of the unique equilibrium point of impulsive Hopfield neural networks with time delays are obtained. Those conditions are more feasible than that given in the earlier references to some extent. Some numerical examples are also discussed in this work to illustrate the advantage of the results we obtained.     

Stability analysis of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms

In this paper, we investigate a class of impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. By establishing an integro-differential inequality with impulsive initial conditions and applying M-matrix theory, we find some sufficient conditions ensuring the existence, uniqueness, global exponential stability and global robust exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with distributed delays and reaction–diffusion terms. An example is given to illustrate the results obtained here.     

Delay-dependent exponential stability for impulsive Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion terms

In this paper, a class of impulsive Cohen–Grossberg neural networks with time-varying delays and reaction–diffusion is formulated and investigated. By employing delay differential inequality and the linear matrix inequality (LMI) optimization approach, some sufficient conditions ensuring global exponential stability of equilibrium point for impulsive Cohen–Grossberg neural networks with time-varying delays and diffusion are obtained. In particular, the estimate of the exponential convergence rate is also provided, which depends on system parameters, diffusion effect and impulsive disturbed intention. It is believed that these results are significant and useful for the design and applications of Cohen–Grossberg neural networks. An example is given to show the effectiveness of the results obtained here.     

Dynamics of fuzzy impulsive bidirectional associative memory neural networks with time-varying delays

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy bidirectional associative memory (BAM) neural networks with impulsive effect and time-varying delays is investigated. The model of fuzzy impulsive BAM neural networks with time-varying delays established as a modified TS fuzzy model is new in which the consequent parts are composed of a set of impulsive BAM neural networks with time-varying delays. Further the exponential stability for fuzzy impulsive BAM neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) technique without tuning any parameters. In addition, an example is provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.     

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.