一类具有垂直传染的传染病模型的稳定性

基于动力系统的理论,讨论了一类具有垂直传染的传染病模型的稳定性.采用下一代矩阵法获得了基本再生数R₀.当R₀<1时,由Routh-Hurwitz判别法,得到了无病平衡点的局部渐近稳定性.通过构造Lyapunov函数,证明了系统在无病平衡点全局渐近稳定.当R₀> 1时,地方病平衡点存在且唯一,借助Routh判据,得出了系统在地方病平衡点局部渐近稳定的条件,并通过构造Lyapunov函数,证明了系统在地方病平衡点全局渐近稳定.最后,用数值模拟验证了结论的合理性.     

具有免疫接种且总人口规模变化的SIR传染病模型的稳定性

讨论一类具有预防免疫接种且有效接触率依赖于总人口的SIR传染病模型,给出了决定疾病灭绝和持续生存的基本再生数σ的表达式,在一定条件下证明了疾病消除平衡点的全局稳定性,得到了唯一地方病平衡点的存在性和局部渐近稳定性条件.最后研究了具有双线性传染率和标准传染率的两个具体模型,并证明了当σ>1时该模型地方病平衡点的全局渐近稳定性.     

一类食饵具有不育控制的捕食模型

讨论了一类食饵具有不育控制的两种群捕食模型,得到了系统平衡点的存在条件,证明了平衡点的局部渐近稳定性和全局稳定性,最后给出了全局稳定的数值模拟,以及对参数进行了分析讨论.     

一年龄结构乙肝传染病模型及稳定性

本文讨论一年龄结构的乙肝传染病模型,得到基本再生数■的表达式,证明当■时,无病平衡点局部渐近稳定且全局渐近稳定;当■时,存在唯一的地方病平衡点,并给出地方病平衡点的局部渐近稳定性条件,这些条件对于控制疾病的传播具有重要的理论及实际意义.     

一类具收获率的功能性反应自抑制三种群模型定性分析

研究了一类具收获率的功能性反应自抑制三种群捕食模型,运用微分方程稳定性理论,确定了捕食系统模型的平衡点存在的条件和性态,得到了系统正平衡点渐近稳定条件,并且分析了平衡点的全局稳定性及系统持续生存的条件.最后利用Matlab软件进行了数值模拟验证.     

一类比率型功能性反应捕食模型的稳定性分析

研究了一类具有比率型功能性反应的捕食模型,对模型进行了定性和稳定性分析,讨论了模型唯一正平衡点的存在条件,以及模型各个平衡点的性态.得到了各个平衡点全局渐近稳定的充分条件.通过绘制模型的相轨线,分析轨线的走向得到了原点全局渐近稳定的条件,并证明了模型不存在非平凡正周期解的条件,通过构造Lyapunov函数得到了模型的唯一正平衡点是全局渐近稳定的结论.     

改进了的捕食者-食饵系统的征税模型分析

探讨了Holling功能性反应的捕食者-食饵征税模型,修改了更合理的捕获函数.讨论了该系统生物经济平衡点的性态,正平衡点的局部渐近稳定性和全局渐近稳定性条件,并利用Pontrjagin最大值原理得到了最优税收策略.为可再生资源的合理开发利用提供了理论依据.     

带有标准发生率和信息干预的时滞SIRS传染病模型的稳定性分析

本文考虑一类具有标准发生率和信息干预的时滞SIRS传染病模型.通过分析模型的特征方程,讨论无病平衡点和地方病平衡点局部渐近稳定性.应用Halanay不等式对无病平衡点的全局渐近稳定性进行证明.通过构造适当的Lyapunov函数讨论地方病平衡点全局渐近稳定性.最后通过数值模拟分析一些重要参数对疾病传播的影响.     

一类多因素HIV模型的研究

研究了一类多因素HIV模型.建立了一个标准型的DI模型,证明了无病平衡点的局部渐近稳定性和全局渐近稳定性.     

一类具有饱和发生率的SIVS吸毒模型的动力学分析

本文研究一类具有饱和传染率的SIVS传染病模型.首先利用Routh-Hurwitz判据和特征根方法,得到平衡点的局部渐近稳定性,其次证明系统的持久性和无病平衡点的全局渐近稳定性,并利用极限系统理论得到地方病平衡点的全局渐近稳定性.最后用数值模拟验证理论结果的正确性.     

Global asymptotic stability analysis for integro-differential systems modeling neural networks with delays

In this paper, the global asymptotic stability of the equilibrium point of integro-differential systems modeling neural networks with time-varying delays are studied. Proper Lyapunov functionals and some analytic techniques are employed to derive the sufficient conditions under which the networks proposed are the global asymptotic stability. The results have shown to improve the previous global asymptotic stability results derived in the literature. Some examples are given to illustrate the correctness of our results.     

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.     

New stability conditions for neural networks with constant and variable delays

In this paper, by utilizing Lyapunov functional method, we analyze global asymptotic stability of neural networks with constant delays. A new sufficient condition ensuring global asymptotic stability of the unique equilibrium point of delayed neural networks is obtained. Furthermore, based on the method of delay differential inequality, the conditions checking global exponential stability of the equilibrium point of neural networks with variable delays are given. The results extend and improve the earlier publications.     

New stability conditions for neural networks with constant and variable delays

In this paper, by utilizing Lyapunov functional method, we analyze global asymptotic stability of neural networks with constant delays. A new sufficient condition ensuring global asymptotic stability of the unique equilibrium point of delayed neural networks is obtained. Furthermore, based on the method of delay differential inequality, the conditions checking global exponential stability of the equilibrium point of neural networks with variable delays are given. The results extend and improve the earlier publications.     

STABILITY ANALYSIS OF HOPFIELD NEURAL NETWORKS WITH DELAYS

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Stability of delayed cellular neural networks

Global asymptotic stability and exponential stability of delayed cellular neural networks is considered in this paper. Based on the Lyapunov stability theorem as well as a fact about the elemental inequality, some new sufficient conditions are given for global asymptotic stability and exponential stability of delayed cellular neural networks. The results are less conservative than those established in the earlier references. Three examples are given to illustrate the applicability of these conditions.     

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．     

具有扩散影响的Hopfield型神经网络的全局渐近稳定性

对具有扩散影响的Hopfield型神经网络平衡点的存在唯一性和全局渐近稳定性进行了研究．在激活函数单调非减、可微且关联矩阵和Liapunov对角稳定矩阵有关时,利用拓扑度理论得到了系统平衡点存在的充分条件．通过构造适当的平均Liapunov函数,分析了系统平衡点的全局渐近稳定性．所得结论表明系统的平衡点(如果存在)是全局渐近稳定的而且也蕴含着系统的平衡点的唯一性．     

Discrete-time analogues of integrodifferential equations modelling bidirectional neural networks

We formulate discrete-time analogues of integrodifferential equations modelling bidirectional neural networks studied by Gopalsamy and He. The discrete-time analogues are considered to be numerical discretizations of the continuous-time networks and we study their dynamical characteristics. It is shown that the discrete-time analogues preserve the equilibria of the continuous-time networks. By constructing a Lyapunov-type sequence, we obtain easily verifiable sufficient conditions under which every solution of the discrete-time analogue converges exponentially to the unique equilibrium. The sufficient conditions are identical to those obtained by Gopalsamy and He for the uniqueness and global asymptotic stability of the equilibrium of the continuous-time network. By constructing discrete-time versions of Halanay-type inequalities, we obtain another set of easily verifiable sufficient conditions for the global exponential stability of the unique equilibrium of the discrete-time analogue. The latter sufficient conditions have not been obtained in the literature of continuous-time bidirectional neural networks. Several computer simulations are provided to illustrate the advantages of our discrete-time analogue in numerically simulating the continuous-time network with distributed delays over finite intervals.     

中立型驱动反应BAM 神经网络的全局渐近同步性

针对一类具有时滞的中立型驱动反应BAM神经网络,提出全局渐近同步性问题.不使用现有文献中传统的李雅普诺夫泛函、矩阵测度和线性矩阵不等式(LMI)等已被广泛应用于研究神经网络全局渐近同步性的方法,而是通过构造2个微分不等式U 1(t)和U 1(t),利用微分不等式方程和不等式技巧解出2个不等式,得到能够确保中立型驱动反应BAM神经网络全局渐近同步的两个新的充分条件.