具有变时滞的神经型Hopfield神经网络的全局吸引子研究

该文探讨了一类具有变时滞的非线性及非自治的神经型Hopfield神经网络的渐近性质.利用非负矩阵的性质和矩阵不等式,得到了保证该系统全局吸引集存在和Lagrange稳定性的充分条件.最后,给出一个例子说明理论的有效性.     

具有可变时滞的非线性非自治差分方程的线性化全局渐近稳定性及其应用

证明了在一定条件下,具有可变时滞的非线性非自治差分方程的全局渐近稳定性可由某种线性差分方程的渐近稳定性确定,给出了这类差分方程全局渐近稳定的充分条件.作为实例,获得了具有可变时滞的离散型非自治广义Log istic方程的全局吸收性判别准则.     

变时滞细胞神经网络稳定性分析

利用矩阵测度、Liapunov函数和Halanay时滞不等式的方法研究了具有变时滞的细胞神经网络模型平衡点的全局指数稳定性问题.给出了判定平衡点全局指数稳定性的几个代数判据,可用于时滞细胞神经网络的设计与检验,数值算例说明其结果的优越性.     

一类非自治随机时滞系统的状态反馈镇定

本文讨论一类非自治随机时滞系统的状态反馈镇定问题.一方面,考虑到模型的特征(复杂非线性、随机以及非自治),本文在反步框架下设计随机时滞系统的时变反馈控制器,给出相应的状态反馈控制策略;另一方面,基于随机Razumikhin稳定性理论来分析系统的依概率全局渐近稳定性,放宽了系统镇定对时滞项的要求.最后,利用数值仿真验证了反馈控制器及控制策略的有效性.     

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.     

一类变时滞Cohen-Grossberg神经网络的全局指数稳定性

研究了一类具变时滞的C ohen-Grossberg神经网络的全局指数稳定性.利用同胚映射理论、Lya-punov函数思想和不等式技巧,给出了平衡点存在唯一性和全局指数稳定性的新的判别准则.     

随机扰动神经网络的脉冲控制

脉冲控制具有响应速度快,鲁棒性和抗干扰能力好的特点,被广泛应用于参数随机扰动的动力学系统的控制.本文研究一类参数随机扰动的变时滞细胞神经网络在脉冲控制下的全局指数稳定性问题.利用Ly印unov稳定性理论和离散Halanay不等式技术手段,分别给出在脉冲控制下,参数随机扰动和无参数扰动的变时滞细胞神经网络全局指数稳定的充分条件.最后,通过数值算例说明所得结果.     

变时滞BAM神经网络的全局指数稳定性

研究一类变时滞BAM神经网络平衡点的全局指数稳定性问题.在不要求激励函数全局Lipschitz条件下,通过构造合适的Lyapunov泛函,并结合Young不等式,得到了BAM神经网络模型在一定条件下全局指数稳定的一些充分条件,推广和改进了前人的相关结论,为综合设计指数稳定的时滞BAM神经网络提供了依据.     

一类变时滞神经网络的全局指数稳定性

本文研究一类变时滞神经网络平衡点的全局指数稳定性.在不要求激活函数全局Lipschitz条件下,利用Lyapunov函数方法,并结合Young不等式和Halanay时滞微分不等式,得到了系统全局指数稳定的充分条件.文末,一个数值例子用以说明本文结果的有效性.     

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。     

Global exponential stability in Lagrange sense for recurrent neural networks with both time-varying delays and general activation functions via LMI approach

In this paper, we study the global exponential stability in a Lagrange sense for recurrent neural networks with both time-varying delays and general activation functions. Based on assuming that the activation functions are neither bounded nor monotonous or differentiable, several algebraic criterions in linear matrix inequality form for the global exponential stability in a Lagrange sense of the neural networks are obtained by virtue of Lyapunov functions and Halanay delay differential inequality. Meanwhile, the estimations of the globally exponentially attractive sets are given out. The results derived here are more general than that of the existing reference. Finally, two examples are given and analyzed to demonstrate our results.     

Stability in Lagrange sense for Cohen–Grossberg neural networks with time-varying delays and finite distributed delays

In this paper, we study the global exponential stability in Lagrange sense for a class of Cohen–Grossberg neural networks with time-varying delays and finite distributed delays. Based on the Lyapunov stability theory, several global exponential attractive sets in which all trajectories converge are obtained. We analyze three different types of activation functions which include both bounded and unbounded activation functions. These results can also be applied to analyze monostable as well as multistable and more extensive neural networks due to making no assumptions on the number of equilibria. Meanwhile, the results obtained in this paper are more general and challenging than that of the existing references. Finally, one example is given and analyzed to verify our results.     

Global lagrange stability of complex‐valued neural networks of neutral type with time‐varying delays

In this article, the problem of global exponential stability in Lagrange sense of neutral type complex‐valued neural networks (CVNNs) with delays is investigated. Two different classes of activation functions are considered, one can be separated into real part and imaginary part, and the other cannot be separated. Based on Lyapunov theory and analytic techniques, delay‐dependent criteria are provided to ascertain the aforementioned CVNNs to be globally exponentially stable GES in Lagrange sense. Moreover, the proposed sufficient conditions are presented in the form of linear matrix inequalities which could be easily checked by Matlab. Finally, two simulation examples are given out to demonstrate the validity of theory results. © 2016 Wiley Periodicals, Inc. Complexity 21: 438–450, 2016     

Global exponential stability in Lagrange sense for recurrent neural networks with time delays

In this paper, we study the global exponential stability in Lagrange sense for continuous recurrent neural networks (RNNs) with multiple time delays. Three different types of activation functions are considered, which include both bounded and unbounded activation functions. By constructing appropriate Lyapunov-like functions, we provide easily verifiable criteria for the boundedness and global exponential attractivity of RNNs. These results can be applied to analyze monostable as well as multistable neural networks.     

Stability analysis in Lagrange sense for a non-autonomous Cohen–Grossberg neural network with mixed delays

The paper discusses the global exponential stability in the Lagrange sense for a non-autonomous Cohen–Grossberg neural network (CGNN) with time-varying and distributed delays. The boundedness and global exponential attractivity of non-autonomous CGNN with time-varying and distributed delays are investigated by constructing appropriate Lyapunov-like functions. Moreover, we provide verifiable criteria on the basis of considering three different types of activation function, which include both bounded and unbounded activation functions. These results can be applied to analyze monostable as well as multistable biology neural networks due to making no assumptions on the number of equilibria. Meanwhile, the results obtained in this paper are more general and challenging than that of the existing references. In the end, an illustrative example is given to verify our results.     

STABILITY IN LAGRANGE SENSE FOR A CLASS OF STOCHASTIC STATIC NEURAL NETWORKS WITH MIXED TIME DELAYS

In this paper,the stability in Lagrange sense of a class of stochastic static neural networks with mixed time delays is studied.Based on the Lyapunov stability theory and with the help of stochastic analysis technique,the criteria for the stability in Lagrange sense of stochastic static neural networks with mixed time delays is obtained.One example is given to verify the advantage and applicability of the proposed results.     

Lagrange exponential stability of quaternion-valued memristive neural networks with time delays

This paper investigates the Lagrange global exponential stability of the quaternion-valued memristive neural networks (QVMNNs). Two kinds of activation functions based on different assumptions are considered. Then, based on the Lyapunov function approach, decomposition method, and some inequality skills, two novel sufficient conditions for lagrange stability of QVMNNs are provided corresponding to different types of activation functions. Lastly, simulation examples are provided to demonstrate the correctness of our theoretical results.     

Stability and stabilization of a class of nonlinear impulsive hybrid systems based on FSM with MDADT

The issue of stability and stabilization for a class of nonlinear impulsive hybrid systems based on finite state machine (FSM) with mode-dependent average dwell time (MDADT) is investigated in this paper. The concepts of global asymptotic stability and global exponential stability are extended for the systems, and the multiple Lyapunov functions (MLFs) are constructed to prove the sufficient conditions of global asymptotic stability and global exponential stability, respectively. Furthermore, the method of stabilization is also given for the hybrid systems. The application of MLFs and MDADT leads to a reduction of conservativeness in contrast with classical Lyapunov function. Finally, a numerical example is given to show the feasibility and effectiveness of the proposed approach.     

Improved global exponential stability for delay difference equations with impulses

In this paper, by means of constructing an impulsive delay difference inequality, we study the global exponential stability for a class of delay difference equations with impulses. A new criterion ensuring the global exponential stability of the equilibrium point is obtained, which is less restrictive and conservative than that given in the earlier reference. An illustrative example is given to demonstrate the effectiveness and advantage of the obtained result.     

Global exponential stability for a class of neural networks

In this paper, the global exponential stability for a class of neural networks is investigated. A simple criterion ensuring global exponential stability is established, which is a less restrictive version of a recent criterion due to Park and Kwon. Some examples showing the effectiveness of the present criterion are given.