基于脉冲和时滞影响的非线性双曲型方程的振动准则

研究一类具高阶Laplace算子的非线性脉冲时滞双曲型偏微分方程的振动性,利用特征函数法和一阶脉冲时滞微分不等式,获得了该类方程在Robin边值条件下所有解振动的若干充分性判据,所得结果推广和包含了最新文献中的结果.     

具有时滞的细胞神经网络模型的全局指数稳定性

利用拓扑度理论、推广的Halanaly矩阵时滞微分不等式、Lyapunov原理以及Dini导数,研究了具有时滞的细胞神经网络模型的全局指数稳定性．去掉了有关文献中要求输出函数fj在实数集R上有界、可微的条件,给出了更弱的判定平衡点的存在唯一性以及全局指数稳定性的判据,推广和改进了前人的相关结论,最后的数值例子说明本文结果不仅保守性小,而且计算简单．     

一类时滞非自治Lotka-Volterra竞争系统的灭绝性

讨论一类具有离散时滞和连续分布时滞的Lotka-Volterra系统,通过构造Lyapunov函数并引入上下平均的概念,将[3]和[6]的方法结合在一起,得到比[6]种群灭绝条件弱的充分条件,同时把文献[3]的结果推广到了时滞非自治系统上.     

变系数变时滞BAM神经网络概周期解的存在性与全局吸引性

利用指数二分性、Banach不动点定理与微分不等式分析技巧,在不要求激活函数有界的条件下,给出了变系数变时滞的BAM神经网络概周期解的存在唯一性和全局吸引性的充分条件．所得结果推广和改进了相应文献的结果。对设计BAM神经网络概周期振荡有重要意义．     

具有反馈控制的非自治多种群捕食-被捕食系统的持久性与全局吸引性

研究具有反馈控制的非自治多种群捕食-被捕食系统的持久性与全局吸引性.通过引入函数上、下平均的概念,得到系统持久和全局吸引的均值条件,同时将文献[7]的结果推广到了时滞非自治系统上.     

一类具有脉冲接种时滞的SEIRS传染病模型

建立了一类具有脉冲接种时滞的SEIRS传染病模型.利用频闪映射和脉冲微分方程比较定理对模型的等价系统进行分析,得到了模型无病周期解具有全局吸引性的存在条件,并且给出了疾病持久性的存在条件.     

具有比率依赖型功能性反应的捕食脉冲系统的持久性（英文）

本文基于经典的阶段结构模型和Lotka-Volterra捕食模型,提出和研究了具有比率依赖型功能性反应模式的脉冲非自治两维时滞微分方程的周期性释放天敌在固定时刻对害虫控制的过程。得到了害虫灭绝周期解的全局吸引性和依赖于时滞和脉冲的人口模型持久性的条件.     

具有时滞的广义BAM脉冲神经网络的全局指数稳定性

本文讨论一类具有时滞的广义双向联想记忆（BAM）脉冲神经网络的指数稳定性．利用Banach空间的不动点定理和Lyapunoy泛函,在不假定激励函数有界、单调和可徽的条件下,得到了平衡点的存在唯一性定理及其为全局指数稳定的充分条件．文末通过实例阐释了所获结论的有效性．     

一类食饵具时滞与扩散的非线性脉冲捕食系统正周期解存在性

主要讨论了一类食饵具有时滞与扩散的非线性脉冲捕食系统正周期解的存在性问题,应用迭合度理论,得到系统存在正周期解的充分条件,推广了没有脉冲时的情形.数值模拟进一步验证了结论的正确性.     

具有脉冲时滞效应两食饵-捕食者Watt型功能反应系统分析

这篇文章应用系统生态数学研究了具有脉冲时滞效应两食饵一捕食者Watt型功能反应的模型.通过应用脉冲方程理论,脉冲比较原理以及一些条件得到了捕食者灭绝周期解存在和全局吸引.然后证明了周期解的持久性而且在该条件下系统至少有一个周期解.     

Exponential synchronization of discontinuous neural networks with time-varying mixed delays via state feedback and impulsive control

This paper investigates drive-response synchronization for a class of neural networks with time-varying discrete and distributed delays (mixed delays) as well as discontinuous activations. Strict mathematical proof shows the global existence of Filippov solutions to neural networks with discontinuous activation functions and the mixed delays. State feedback controller and impulsive controller are designed respectively to guarantee global exponential synchronization of the neural networks. By using Lyapunov function and new analysis techniques, several new synchronization criteria are obtained. Moreover, lower bound on the convergence rate is explicitly estimated when state feedback controller is utilized. Results of this paper are new and some existing ones are extended and improved. Finally, numerical simulations are given to verify the effectiveness of the theoretical results.     

一类具变时滞的模糊BAM神经网络的平衡点的指数稳定性(英文)

研究一类具变时滞的模糊BAM神经网络.利用拓扑度论和微分不等式,获得了该类网络平衡点的存在性、唯一性和全局指数稳定性的充分条件.一个例子用来解释本文获得的结果.     

Attractability and location of equilibrium point of cellular neural networks with time-varying delays

This paper presents new theoretical results on global exponential stability of cellular neural networks with time-varying delays. The stability conditions depend on external inputs, connection weights and delays of cellular neural networks. Using these results, global exponential stability of cellular neural networks can be derived, and the estimate for location of equilibrium point can also be obtained. Finally, the simulating results demonstrate the validity and feasibility of our proposed approach.     

具有变时滞竞争神经网络的指数稳定性

利用微分方程组的基解矩阵及推广的Halanay微分不等式等分析技巧,讨论了一类具有不同时间尺度的变时滞竞争神经网络的平衡点存在和唯一性,并给出指数稳定性判定的充分条件,最后通过数值仿真实例检验结果的正确性.     

Mean square exponential and robust stability of stochastic discrete-time genetic regulatory networks with uncertainties

This paper aims to analyze global robust exponential stability in the mean square sense of stochastic discrete-time genetic regulatory networks with stochastic delays and parameter uncertainties. Comparing to the previous research works, time-varying delays are assumed to be stochastic whose variation ranges and probability distributions of the time-varying delays are explored. Based on the stochastic analysis approach and some analysis techniques, several sufficient criteria for the global robust exponential stability in the mean square sense of the networks are derived. Moreover, two numerical examples are presented to show the effectiveness of the obtained results.     

多比例时滞细胞神经网络的指数周期性与稳定性

研究了一类具多比例时滞细胞神经网络的全局指数周期性与稳定性.通过变换y(t)=x(e~t)将具多比例时滞的细胞神经网络变换成具常时滞变系数的细胞神经网络,利用一些分析技巧与构造合适的Lyapunov泛函,得到系统的周期解存在唯一且全局指数周期的时滞依赖的充分条件,判断方法简单易验证.并给出了两个例子及其数值仿真结果以支持所得结论.     

变时延神经网络的全局指数稳定性及收敛性

利用Lyapunov泛函方法和线性矩阵不等式（LMI）技术,通过引入一系列参数,给出全局指数稳定的平衡点的判别条件和时延的最大上界和神经网络的收敛速度,所得结果较之一些文献中的结果简单、实用并且对于具体设计带时延神经网络有重要的指导意义.最后,通过实例表明给出的判定条件是有效、可行的.     

Exponential synchronization of memristive Cohen–Grossberg neural networks with mixed delays

This paper concerns the problem of global exponential synchronization for a class of memristor-based Cohen–Grossberg neural networks with time-varying discrete delays and unbounded distributed delays. The drive-response set is discussed. A novel controller is designed such that the response (slave) system can be controlled to synchronize with the drive (master) system. Through a nonlinear transformation, we get an alternative system from the considered memristor-based Cohen–Grossberg neural networks. By investigating the global exponential synchronization of the alternative system, we obtain the corresponding synchronization criteria of the considered memristor-based Cohen–Grossberg neural networks. Moreover, the conditions established in this paper are easy to be verified and improve the conditions derived in most of existing papers concerning stability and synchronization for memristor-based neural networks. Numerical simulations are given to show the effectiveness of the theoretical results.