广义Lyapunov函数与微分系统关于两种量度的相对稳定性(Ⅲ)

讨论具有性质（V）的广义Lyapunov函数的确定与判别，从而基本上完成利用广义Lyapunov函数研究微分系统关于两种量度的相对稳定性问题．     

具有反馈控制的两种群竞争系统的概周期解

讨论了具有反馈控制的两种群概周期竞争系统,利用微分不等式和构造适当的Lyapunov函数,获得存在全局渐近稳定的概周期解的充分条件.     

时标上脉冲混合动态系统实用渐近稳定性（英文）

在此篇论文中,采用Lyapunov直接法研究了一类时标脉冲混合动态系统关于两个测度的实用稳定性,进而将多个Lyapunov函数法推广到实用渐近稳定的研究中,并得到了相应的结论.     

带脉冲的BAM神经网络的全局指数稳定性

利用M矩阵理论,推广的微分不等式和Lyapunov函数,研究了一类带时滞和脉冲的BAM神经网络平衡点的存在唯一性和全局指数稳定性条件.文中推广了以往文献脉冲函数的形式,无需时滞的可导性要求,从而减弱了以往结论的条件,并且可以估计网络的指数收敛速率.     

一类双线性生态系统的无源控制

研究了一类广义双线性系统的无源控制问题,利用广义Lyapunov函数和线性矩阵不等式,给出了广义双线性系统无源且零解渐近稳定的充分条件,并在一定条件下得到存在状态反馈控制器,使得闭环系统无源且零解渐近稳定的充分条件,同时给出相应的控制器构造方法。     

具时滞和阶段结构的非自治捕食系统的持续性与全局稳定性

研究了一类具有时滞和阶段结构的HollingⅣ型捕食系统的持续性,通过构造合适的Lyapunov函数讨论了这类系统的全局稳定性.     

具有反馈控制和功能性反应的两种群竞争系统的概周期解

研究了具有反馈控制和功能性反应的两种群竞争系统．通过构造适当的Lyapunov函数,得到了系统存在全局渐进稳定的概周期解的充分性条件。     

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．     

广义Kolmogorov模型的Lyapunov函数构造新算法及其应用

本文对广义Kolmogorov模型,给出构造Lyapunov函数的新算法,在文献1中只对其中某些特殊类型给出几种特殊的构造方法,而本文给出的是这类模型的一般新算法,应用较广泛。     

具有反馈控制的Lotka-Volterra捕食-食饵系统研究

研究具有反馈控制的两种群Lotka-Volterra捕食系统平衡点的稳定性.通过构造适当的Lyapunov函数分别获得一组保证正平衡点和边界平衡点全局吸引的充分性条件.研究表明针对我们所采取的反馈控制策略,捕食者种群绝灭的风险加大,其可能的原因在于随着对食饵种群干扰力度的加大,捕食者种群将难以获得足够的食物,从而导致绝灭.     

高阶S-分布时滞广义细胞神经网络的全局指数稳定性

研究一类高阶S-分布时滞广义细胞神经网络的稳定性.利用一些分析技巧,通过构造恰当的Lebesgue-Stieltjes积分型Lyapunov泛函,得到系统全局指数稳定的充分条件,判断方法简单易验证.最后给出了主要定理的一个实例,表明结论的有效性.     

Existence and global asymptotic stability of positive periodic solutions of n-species delay impulsive Lotka–Volterra type systems

In this paper, the existence and global asymptotic stability of positive periodic solutions of periodic n-species Lotka–Volterra impulsive systems with several deviating arguments are studied. By using the continuation theorem of coincidence degree theory and Lyapunov–Razumikhin method, sufficient conditions are obtained. Some known results are improved and generalized.     

Invariant distributions for multi-population models in random environments

We obtain the existence of a solution and invariant distribution for systems of stochastic differential equations which represent populations in random environments. The method used is a stochastic Lyapunov function, based on a theorem of Kushner. The method is applied to a system of two populations exchainging individuals through migration, and to a generalized n-dimensional Lotka-Volterra system.     

Local synchronization of one-to-one coupled neural networks with discontinuous activations

In this paper, local synchronization is considered for coupled delayed neural networks with discontinuous activation functions. Under the framework of Filippov solution and in the sense of generalized derivative, a novel sufficient condition is obtained to ensure the synchronization based on the Lyapunov exponent and the detailed analysis in Danca (Int J Bifurcat Chaos 12(8):1813–1826, 2002; Chaos Solitons Fractals 22:605–612, 2004). Simulation results are given to illustrate the theoretical results.     

Complex Generalized Synchronization and Parameter Identification of Nonidentical Nonlinear Complex Systems

In this paper, generalized synchronization (GS) is extended from real space to complex space, resulting in a new synchronization scheme, complex generalized synchronization (CGS). Based on Lyapunov stability theory, an adaptive controller and parameter update laws are designed to realize CGS and parameter identification of two nonidentical chaotic (hyperchaotic) complex systems with respect to a given complex map vector. This scheme is applied to synchronize a memristor-based hyperchaotic complex Lü system and a memristor-based chaotic complex Lorenz system, a chaotic complex Chen system and a memristor-based chaotic complex Lorenz system, as well as a memristor-based hyperchaotic complex Lü system and a chaotic complex Lü system with fully unknown parameters. The corresponding numerical simulations illustrate the feasibility and effectiveness of the proposed scheme.     

Research on cascading high-dimensional isomorphic chaotic maps

In order to overcome the security weakness of the discrete chaotic sequence caused by small Lyapunov exponent and keyspace, a general chaotic construction method by cascading multiple high-dimensional isomorphic maps is presented in this paper. Compared with the original map, the parameter space of the resulting chaotic map is enlarged many times. Moreover, the cascaded system has larger chaotic domain and bigger Lyapunov exponents with proper parameters. In order to evaluate the effectiveness of the presented method, the generalized 3-D Hénon map is utilized as an example to analyze the dynamical behaviors under various cascade modes. Diverse maps are obtained by cascading 3-D Hénon maps with different parameters or different permutations. It is worth noting that some new dynamical behaviors, such as coexisting attractors and hyperchaotic attractors are also discovered in cascaded systems. Finally, an application of image encryption is delivered to demonstrate the excellent performance of the obtained chaotic sequences.     

Existence and global attractivity of positive periodic solutions of periodic n-species Lotka-Volterra competition systems with several deviating arguments.

In this paper, we study the existence and global attractivity of positive periodic solutions of periodic n-species Lotka-Volterra competition systems. By using the method of coincidence degree and Lyapunov functional, a set of easily verifiable sufficient conditions are derived for the existence of at least one strictly positive (componentwise) periodic solution of periodic n-species Lotka-Volterra competition systems with several deviating arguments and the existence of a unique globally asymptotically stable periodic solution with strictly positive components of periodic n-species Lotka-Volterra competition system with several delays. Some new results are obtained. As an application, we also examine some special cases of the system we considered, which have been studied extensively in the literature. Some known results are improved and generalized.     

An information-theoretical approach to a system of interacting elements

The maximum-entropy principle in information theory is generalized to include the interaction between elements of the system. A complex relation between the probabilities of the events is derived using a familiar technique in statistical mechanics. The relation is explicitly discussed for the case of bilinear interaction and only two events. Quite noteworthy is the existense in the system of a kind of “phase transition” similar to ferromagnetism. The result is applied to the mass behaviour. It is shown that the cooperative mass behaviour such as boom and fashion may be interpreted as a phase transition which would occur below certain “informational temperature”.     

Finite dimensional structure of the GPI discharge in patients with Parkinson's disease

Stochastic systems are infinitely dimensional and deterministic systems are low dimensional, while real systems lie somewhere between these two limit cases. If the calculation of a low (finite) dimension is in fact possible, one could conclude that the system under study is not purely random. In the present work we calculate the maximal Lyapunov exponent from interspike intervals time series recorded from the internal segment of the Globus Pallidusfrom patients with Parkinson's disease. We show the convergence of the maximal Lyapunov exponent at a dimension equal to 7 or 8, which is therefore our estimation of the embedding dimension for the system. For dimensions below 7 the observed behavior is what would be expected from a stochastic system or a complex system projecting onto lower dimensional spaces. The maximal Lyapunov exponent did not show any differences between tremor and akineto-rigid forms of the disease. However, it did decay with the value of motor Unified Parkinson's Disease Rating Scale -OFF scores. Patients with a more severe disease (higher UPDRS-OFF score) showed a lower value of the maximal Lyapunov exponent. Taken together, both indexes (the maximal Lyapunov exponent and the embedding dimension) remark the importance of taking into consideration the system's non-linear properties for a better understanding of the information transmission in the basal ganglia.