不对称时延Hopfield神经网络的全局指数稳定性及收敛速度的估计

本文通过构造适当Ｌｙａｐｕｎｏｖ泛函的方法,对具有时延的不对称Ｈｏｐｆｉｅｌｄ型神经网络平衡点的指数稳定性进行了分析,得到了平衡点指数稳定的充分条件,同时我们也对其指数收敛速度进行了估计．     

不对称时处Holpfield神经网络的全局指数稳定性及收敛速度的估计

本文通过构造适当Ｌｙａｐｕｎｏｖ泛函的方法,对具有时延的不对称Ｈｏｐｆｉｅｌｄ型神经网络平衡点的指数稳定性进行了分析,得到了平衡点指数的稳定充分条件,同时我们也对其指数收敛速度进行了估计。     

连续双向联想记忆网络局部指数稳定的充要条件

本文得到了连续双向联想记忆神经网络平衡点局部指数稳定的充要条件及平衡点指数吸收域的估计,所得结论对连续ＢＡＭ神经网络的设计及应用都很有意义。     

二阶Hopfield型神经网络的稳定性 分析及收敛速度的估计

本文讨论二阶连续Hopfield型神经网络平衡点的全局稳定性问题,利用LMI方法和Lyapunov方法得到了网络平衡点全局渐近稳定和全局指数稳定的几个充分条件,并对其指数收敛速度进行了估计.     

连续非对称神经网络的动力学特性

本文讨论了连续非对称神经网络的动力学特性,给出了神经网络有唯一平衡点的条件,并讨论了当连接距阵T变化时不产生静态分叉和Hopf分叉的条件,给出了平衡点的全局渐近稳定性和全局指数稳定性的充分条件。     

一类具多比例延时的细胞神经网络的指数稳定性

讨论了一类具多比例延时的细胞神经网络的指数稳定性.利用非线性测度得到了一个保证平衡点存在唯一且指数稳定的充分条件,并给出了解的指数收敛速度.最后验证了结论的正确性并进行了模拟仿真.     

连续非对称神经网络的动力学特性

本文讨论了连续非对称神经网络的动力学特性，给出了神经网络有唯一平衡点的条件，并讨论了当连接距阵Ｔ变化时不产生静态分叉和Ｈｏｐｆ分叉的条件，给出了平衡点的全局渐近稳定性和全局指数稳定性的充分条件。     

广义的时滞细胞神经网络的动态分析

本文研究了广义细胞神经网络的动态行为,首先利用度理论建立了客中广义时滞细胞神经网络的平衡点存在唯一物充要条件,其次给出这种广义时滞神经网络全局指数稳定的充要条件。     

中立型时滞BAM神经网络的稳定性分析

文章介绍了中立型时滞BAM神经网络,并重点分析其稳定性,给出了中立型时滞BAM神经网络的平衡点唯一和全局指数稳定性的判据,同时举例说明判据的正确性和结果的有效性。     

神经网络指数稳定性分析及收敛率估计

该文通过李雅普诺夫直接方法,研究了一类 Hopfield神经网络平衡点的存在性、唯一性与指数稳定性。文中假设神经网络系统的激励函数为单调增Lipschitz连续函数,在自反馈项为非线性函数的条件下,研究其指数稳定性,同时给出了收敛率估计式。     

Sufficient and necessary conditions for global exponential stability of discrete-time recurrent neural networks

A set of sufficient and necessary conditions are presented for global exponential stability (GES) of a class of generic discrete-time recurrent neural networks. By means of the uncovered conditions, GES and convergence properties of the neural networks are analyzed quantitatively. It is shown that exact equivalences exist among the GES property of the neural networks, the contractiveness of the deduced nonlinear operators, and the global asymptotic stability (GAS) of the neural networks plus the spectral radius of Jacobian matrix of the neural networks at the unique equilibrium point less than one. When the neural networks have small state feedback coefficients, it is shown further that the infimum of exponential bounds of the trajectories of the neural networks equals exactly the spectral radius of Jacobian matrix of the neural networks at the unique equilibrium point. The obtained results are helpful in understanding essence of GES and clarifying difference between GES and GAS of the discrete-time recurrent neural networks.     

New results for exponential stability of delayed cellular neural networks

This brief presents new sufficient conditions for the global exponential stability of the equilibrium point for delayed cellular neural networks (DCNNs). It is shown that the use of a more general type of Lyapunov-Krasovskii functional enables us to derive new results for exponential stability of the equilibrium point for DCNNs. The results establish a relation between the delay time and the parameters of the network. The results are also compared with one of the most recent results derived in the literature.     

Global Exponential Stability Analysis of a Class of Dynamical Neural Networks

The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point is presented. And the upper bound for the degree of exponential stability is given. Moreover, a simulation is given to show the effectiveness of the result.     

Global Exponential Stability Analysis of a Class of Dynamical Neural Networks

The problem of the global exponential stability of a class of Hopfield neural networks is considered. Based on nonnegative matrix theory, a sufficient condition for the existence, uniqueness and global exponential stability of the equilibrium point is presented. And the upper bound for the degree of exponential stability is given. Moreover, a simulation is given to show the effectiveness of the result.     

高阶Hopfield型神经网络的定性分析

本文讨论高阶连续型Hopfield神经网络平衡态的性质,包括平衡态的存在唯一性和稳定性.借助于Banach不动点原理和Lyapunov方法得到了若干平稳态存在唯一和全局渐近稳定的充分条件.作为特例,获得了一阶连续型Hopfield神经网络稳定性的新判据.     

Stabilizing Effects of Impulses in Delayed BAM Neural Networks

This brief studies the stabilizing effects of impulses in delayed bidirectional associative memory (DBAM) neural networks when its continuous component does not converge asymptotically to the equilibrium point. A general criterion, which characterizes the aggregated effects of the impulse and the deviation of its continuous component from the equilibrium point on the exponential stability of the considered DBAM, is established by using Lyapunov–Razumikhin technique. It is shown that because of shrinking effects of impulse the DBAM may be globally exponentially stable even if the evolution of its continuous component deviates from the equilibrium point.