EXPONENTIAL STABILITY OF DISCRETE-TIME CELLULAR NEURAL NETWORKS WITH DELAYS

In this paper, a new sufficient condition for the global exponential stability of a unique equilibrium point of discrete-time cellular neural networks is given. It is shown that the condition relies on the feedback matrices and is independent of the delay parameter. Furthermore, this condition is less restrictive than those given in the literature.     

STABILITY OF DISCRETE-TIME COHEN-GROSSBERG BAM NEURAL NETWORKS WITH DELAYS

In this paper, we study the existence and stability of an equilibrium of discrete-time Cohen-Grossberg BAM Neural Networks with delays. We obtain several sufficient conditions ensuring the existence and stability of an equilibrium of such systems, using discrete Halanay-type inequality and vector Lyapunov methods. In addition, we show that the proposed sufficient condition is independent of the delay parameter. An example is given to demonstrate the effectiveness of the results obtained.     

THE STABILITY OF A KIND OF DISCRETE-TIME HOPFIELD NEURAL NETWORKS WITH ASYMPTOTICAL WEIGHTED MATRICES

In this paper, the authors analyze the stability of a kind of discrete-time Hopfield neural network with asymptotical weighted matrix, which can be expressed as the product of a positive definite diagonal matrix and a symmetric matrix. We obtain that it has asymptotically stable equilibriums if the network is updated asynchronously, and asymptotically stable equilibriums or vibrating final stage with 2 period if updated synchronously. To prove these, Lassale's invariance principle in difference equation is applied.     

ON GLOBAL ROBUST STABILITY FOR INTERVAL HOPFIELD NEURAL NETWORKS WITH TIME DELAY

The theorem obtained by Liao was not true (see [2]). So, this paper presents some criteria of global robust stability for interval Hopfield neural networks with time delay. The methods to judge the robust stability are practical and easily verifiable.     

SCALE-TYPE STABILITY FOR NEURAL NETWORKS WITH UNBOUNDED TIME-VARYING DELAYS

This paper studies scale-type stability for neural networks with unbounded time-varying delays and Lipschitz continuous activation functions. Several sufficient conditions for the global exponential stability and global asymptotic stability of such neural networks on time scales are derived. The new results can extend the existing relevant stability results in the previous literatures to cover some general neural networks.     

PERIODIC SOLUTIONS AND STABILITY ANALYSIS FOR NEURAL NETWORKS WITH TIME-VARYING DELAY

1 IntroductionIt is well known that both in biological and man-made neural systems, inte-gl.ation afld communication delays are ubiquitous, and often become sources ofinstabilitY' The de1ays in electronic neural networks are usually time varying,and sometimes vary vio1ently with time due to the finite switching speed ofamplifiers and faults in the electrical circuit. They s1ow down the transmissionrate and tend to introduce some degree of instability in circuits. Therefore,fast response must …     

ASYMPTOTIC BEHAVIOR IN NONLINEAR DISCRETE-TIME NEURAL NETWORKS WITH DELAYED FEEDBACK

This paper is concerned with a delay difference system. Some interesting results are obtained for the asymptotic behaviors of the system. Our theorems improve the corresponding theorems in the relevant literature by removing the restriction of the initial conditions.     

变时滞Hopfield神经网络的全局指数稳定性

本文研究了一类具有变时滞Hopfield神经网络的稳定性问题.利用时滞微分不等式方法,获得了几个关于该网络的全局指数稳定性与时滞无关的充分条件,并且给出了此类网络的收敛指数的估计,推广了已知文献的结果.最后给出数值例子证明结论的有效性.     

NEW CRITERIA OF EXPONENTIAL STABILITY FOR BAM NEURAL NETWORKS WITH DELAYS

In this Letter,a novel Lyapunov functional is constructed to investigate the exponential stability of the BAM neural networks.New sufficient conditions of the uniqueness and global exponential stability for the equilibrium of BAM neural networks with delays are obtained.The results improve those existing ones.     

GLOBAL ROBUST STABILITY OF INTERVAL HOPFIELD NEURAL NETWORKS WITH CONTINUOUSLY DISTRIBUTED DELAYS

In this paper,we implement topological degree theory and Lyapunov-functional methods to obtain the existence and uniqueness of the equilibrium point and its global robust stability for interval Hopfield neural networks with continuously distributed delays.Moreover,the methods used in judging the robust stability are proven practical and easily verifiable.     

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。     

GLOBALLY ASYMPTOTIC STABILITY OF MULTI-DELAYED NEURAL NETWORKS

A sufficient condition is presented for the uniqueness and globally asymptotic stability of a class of neural networks with multiple time-varying delays. The result is less conservative than some recent results in the literatures.     

ON GLOBAL ROBUST STABILITY FOR COMPETITIVE NEURAL NETWORKS WITH TIME DELAYS AND DIFFERENT TIME SCALES

In this paper, using the theory of topological degree and Liapunov functional methods, the authors study the competitive neural networks with time delays and different time scales and present some criteria of global robust stability for this neural network model.     

具无限变时滞的神经网络的稳定性分析

本文研究了具无限变时滞的神经网络的全局指数稳定性，在假设神经元输出输入活化函数有界和满足全局Lipschitz条件下，得到了神经网络具唯一平衡点且该平衡点全局指数稳定的一些充分条件，推广了已有文献中无时滞的相应结果。     

具有外部输入和偏差的细胞神经网络的完全稳定性

本文证明了对于由两个细胞组成的细胞神经网络，外部输入和偏差不影响网络的稳定平衡点的存在性和完全稳定性．     

一类具分布时滞的Hopfield网络模型的概周期解存在性与稳定性

利用Banach空间压缩不动点定理和微分不等式的分析技巧,研究一类具分布时滞的Hopfield神经网络模型的概周期解的存在性和稳定性。得到了系统存在概周期和全局指数稳定的新的充分条件。所得结果改进和推广了相关文献的结论。     

EXPONENTIAL STABILITY AND PERIODIC SOLUTION OF HYBRID BIDIRECTIONAL ASSOCIATIVE MEMORY NEURAL NETWORKS WITH DISCRETE DELAYS

In this paper, we study the existence, uniqueness, and the global exponential stability of the periodic solution and equilibrium of hybrid bidirectional associative memory neural networks with discrete delays. By ingeniously importing real parameters di > 0 (i = 1,2, …, n) which can be adjusted, making use of the Lyapunov functional method and some analysis techniques, some new sufficient conditions are established. Our results generalize and improve the related results in [9]. These conditions can be used both to design globally exponentially stable and periodical oscillatory hybrid bidirectional associative neural networks with discrete delays, and to enlarge the area of designing neural networks. Our work has important significance in related theory and its application.     

具变时滞的细胞神经网络的周期解

本文研究了一类具变时滞细胞神经网络系统,利用重合度理论中的延拓定理、不等式的分析原理、矩阵理论和Lyapunov泛函方法,得到了该系统周期解的存在唯一性及全局指数稳定性的结论,不仅在一些应用领域上是可行的且易检验,而且为神经网络系统周期解的存在性论证,提供了一个新的有效方法.     

EXISTENCE AND STABILITY OF PERIODIC SOLUTIONS OF DELAYED BAM NEURAL NETWORKS WITH PERIODIC COEFFICIENTS

In this paper, we study the BAM neural networks with variable coefficients and delays. By using the Banach fixed point theorem and constructing suitable Lyapunov function, we obtain some sufficient conditions ensuring the existence, uniqueness and global stability of periodic solution. These results are helpful to design global exponential stable BAM networks and periodic oscillatory BAM networks.     

PERIODIC SOLUTIONS TO FUZZY CELLULAR NEURAL NETWORKS WITH DISTRIBUTED DELAYS

In this paper, a class of fuzzy cellular neural networks with distributed delays is discussed. By employing fixed point theorem and inequality techniques, some sufficient conditions are obtained to ensure the existence and global exponential stability of periodic solutions to the systems. Without assuming the global Lipschitz conditions of activation functions, our results are novel and reduce the limitation of previous known results. Moreover, an example is given to illustrate the effectiveness of our resu...