基于时滞反馈控制的混沌时滞神经网络的自适应同步（英文）

研究两个混沌时滞神经网络在加入一个新的自适应控制器的条件下达到同步的问题.通过构造一个新的李雅普诺夫函数并结合李雅普诺夫稳定性原理、LMI工具箱和自适应反馈控制原理,得到了两个混沌时滞神经网络自适应同步的条件.最后,给出相应的数值模拟来验证所得结论的有效性.     

一类带有Neumann边界条件奇异摄动反应扩散方程的周期解

主要研究一类奇异摄动反应扩散方程周期解的存在性和渐近稳定性.首先,利用边界层函数法,构造出形式渐近解,基于微分不等式理论,得到了周期解的存在性.然后讨论周期解在李雅普诺夫意义下的渐近稳定性.最后,由具体例子说明该方法的有效性.     

具有概周期系数的三种群第Ⅱ类功能性反应的全局渐近稳定性

讨论了一类具有概周期系数的三种群第Ⅱ类功能性反应的模型，通过利用微分不等式及构造适当的李雅普诺夫函数获得了其存在全局渐近稳定性的概周期解的充分条件．     

一类扩张的细胞神经网络稳定性判别法

本文研究一类含有阻尼项带有时滞细胞神经网络的全局渐进稳定性和一致稳定性的性质,通过构造适当的李雅普诺夫函数及利用分析的有关知识,给出了全局渐进稳定性和一致稳定的判别法．     

四阶非线性系统的全局渐近稳定性

运用构造李雅普诺夫函数的方法 ,研究了一类四阶非线性系统的全局渐近稳定性 ,给出了该系统零解全局渐近稳定的充分条件     

一类四阶非线性系统的李雅普诺夫函数构造和零解稳定性

计算出了四阶常系数线性系统的各种形式的李雅普诺夫函数,并将四阶非线性系统化成它的等价系统,通过类比的方法构造出一类四阶非线性系统的李雅普诺夫函数,从而获得该系统零解全局渐近稳定的充分条件.     

一类四阶非线性系统的稳定性

本文利用“类比法”构造了一类四阶非线性系统的李雅普诺夫函数 ,导出了该系统的平凡解的稳定性条件     

论李雅普诺夫函数的构造

А.М.Ляпунов稳定性理论中的一个核心问题,就是李雅普诺夫函数的构造问题。尽管近卅年来人们作了不少的努力,但直到现在为止,对于一般非线性系统而言,还是没有构造其李雅普诺夫函数的通用而有效的方法。虽然如此,但针对具体问题进行具体分析,也就是针对实际中出现的各种非线性系统,通过定性分析,然后根据实际情况,构造出恰当的李雅普诺夫函数,就这一点而论,还是取得了极其丰富的成果。因此在探索非线性     

四阶非线性系统的全局部渐近稳定性

运用构造李雅普诺夫函数的方法,研究了一类四阶非线性系统的全局部渐近稳定性,给出了该系统零解全局渐近稳定的充分条件。     

灰色中立随机线性时滞系统的稳定性分析(英文)

通过使用灰色矩阵覆盖集的分解方法和矩阵范数的性质,构造李雅普诺夫函数,研究了灰色中立随机线性时滞系统的鲁棒稳定性和几乎指数鲁棒稳定性.     

Anti-periodic solution for delayed cellular neural networks with impulsive effects

In this paper, we discuss anti-periodic solution for delayed cellular neural networks with impulsive effects. By means of contraction mapping principle and Krasnoselski’s fixed point theorem, we obtain the existence of anti-periodic solution for neural networks. By establishing a new impulsive differential inequality, using Lyapunov functions and inequality techniques, some new results for exponential stability of anti-periodic solution are obtained. Meanwhile, an example and numerical simulations are given to show that impulses may change the exponentially stable behavior of anti-periodic solution.     

Existence and global exponential stability of almost periodic solution for quaternion-valued high-order Hopfield neural networks with delays via a direct method

This paper deals with the existence and global exponential stability of almost periodic solutions for quaternion-valued high-order Hopfield neural networks with delays by a direct approach. Based on the contraction mapping principle, sufficient conditions are derived to ensure the existence and uniqueness of almost periodic solutions for the networks under consideration. By constructing a suitable Lyapunov function, the global exponential stability criterion of the almost periodic solution are derived. Finally, two numerical examples are given to illustrate the main results of this paper.     

Existence and global exponential stability of anti‐periodic solutions for delayed quaternion‐valued cellular neural networks with impulsive effects

In this paper, we consider a class of delayed quaternion‐valued cellular neural networks (DQVCNNs) with impulsive effects. By using a novel continuation theorem of coincidence degree theory, the existence of anti‐periodic solutions for DQVCNNs is obtained with or without assuming that the activation functions are bounded. Furthermore, by constructing a suitable Lyapunov function, some sufficient conditions are derived to guarantee the global exponential stability of anti‐periodic solutions for DQVCNNs. Our results are new and complementary to the known results even when DQVCNNs degenerate into real‐valued or complex‐valued neural networks. Finally, an example is given to illustrate the effectiveness of the obtained results.     

Periodic solutions of nonautonomous cellular neural networks with impulses and delays

By using the continuation theorem of coincidence degree theory and constructing suitable Lyapunov functions, we study the existence, uniqueness and global exponential stability of periodic solutions for nonautonomous cellular neural networks with impulses and delays. Further, using the numerical simulation method the influence of the impulsive perturbations on the inherent oscillation is investigated. The numerical simulation shows that our models can occur in many forms of complexities including periodic oscillation and Gui chaotic strange attractor.     

Analysis of global exponential stability and periodic solutions of FCNNs with constant delays and time-varying delays

In this paper, we investigate a class of fuzzy cellular neural networks with constant delays and time-varying delays. By constructing suitable Lyapunov functional and employing Young inequality, we find sufficient conditions for the existence, uniqueness, global exponential stability of equilibrium, and the existence of periodic solutions of fuzzy cellular neural networks with time-varying delays. The results of this paper are new and they extend previously known results.     

Dynamics of bidirectional associative memory networks with distributed delays and reaction–diffusion terms

In this paper, the periodic oscillatory solution and stability are investigated for a class of bidirectional associative memory neural networks with distributed delays and reaction–diffusion terms. By constructing a new Lyapunov functional, applying M-matrix theory and inequality technique, several novel sufficient conditions are derived to ensure the existence and uniqueness of periodic oscillatory solutions for bidirectional associative memory neural networks with distributed delays and reaction–diffusion terms, and all other solutions of this network converge exponentially to the unique periodic oscillatory solution. Moreover, the exponential convergence rate is estimated, which depends on the delay kernel functions and the system parameters. Two numerical examples are given to show the effectiveness of the obtained results. The results extend and improve the previously known results.     

BAM-type impulsive neural networks with time-varying delays

By using the continuation theorem of coincidence degree theory and constructing a suitable Lyapunov functional, we derive some sufficient conditions for the existence and global exponential stability of a unique periodic solution of BAM neural networks, which assumes neither the monotony nor the boundedness of the activation functions. It is believed that these results are significant and useful for the design and applications of BAM neural networks.     

Global exponential stability and existence of periodic solutions in BAM networks with delays and reaction–diffusion terms

Both exponential stability and periodic solutions are considered for a class of bi-directional associative memory (BAM) neural networks with delays and reaction–diffusion terms by constructing suitable Lyapunov functional and some analysis techniques. The general sufficient conditions are given ensuring the global exponential stability and existence of periodic solutions of BAM neural networks with delays and reaction–diffusion terms. These presented conditions are in terms of system parameters and have important leading significance in the design and applications of globally exponentially stable and periodic oscillatory neural circuits for BAM with delays and reaction–diffusion terms.     

Existence and global exponential stability of periodic solution of a class of neural networks with impulses

Sufficient conditions are obtained for the existence and global exponential stability of a unique periodic solution of a class of neural networks with variable and unbounded delays and impulses by using Mawhin’s continuation theorem of coincidence degree theory and by constructing Lyapunov functions.