图论与复杂网络

近10年来迅猛发展起来的复杂网络理论为研究复杂性与复杂系统科学提供了一个重要支撑点,它高度概括了复杂系统的重要特征,无论是在理论还是在应用方面都具有很强的生命力,而且在各个方面都得到了很大发展.重点讨论图论在复杂网络中的应用,特别是代数图论在复杂网络同步问题中的应用.首先给出一些图的最小非零与最大特征值以及同步能力的估计,并且讨论了子图与图特征向量在同步能力估计中的作用.其次以两个简单图指出同步能力与网络结构参数的关系复杂,并给出补图与加边对同步研究的意义,然后给出图运算在复杂网络同步中的作用.最后从图论与控制理论角度展望了复杂网络领域未来可能的发展方向.      

生物神经元系统同步转迁动力学问题

生物神经元系统中存在着丰富的同步模式, 不同同步模式的实现条件已经被广泛地研究. 然而, 不同同步模式之间的转迁是神经动力学研究领域的难点问题, 近年来在此方面开展了许多相应的研究工作. 本文主要阐述近年来在神经元系统同步转迁动力学方面的研究进展, 揭示神经元系统在耦合、时滞和网络拓扑等不同参数作用下呈现的复杂的同步转迁动力学行为及其可能的动力学机制. 最后总结研究进展的内容并提出对同步动力学今后研究的展望.     

基于脉冲控制下的复杂时滞动力网络的鲁棒同步

近年来复杂网络已经引起了科学和工程技术等各个领域的广泛关注,尤其是复杂网络中的非线性动力学行为,以及网络的拓扑结构如何影响它的动力学行为的研究,已成为当前一项极其重要的战略课题.本文主要讨论基于脉冲控制下复杂时滞动力网络的同步动力学,应用时滞动力系统的脉冲控制理论,给出了复杂时滞动力网络的一些简单而又一般的鲁棒同步化准则,这些准则能够提供一个新的和有效的控制方法来同步一个任意给定的时滞动力网络到一个期望的同步态,进一步地将所获得的结果应用到由混沌FHN神经元振子为动力节点所构成的一个具有近邻耦合结构的复杂动力网络,数值模拟表明了所获理论结果的正确性和控制方法的有效性.     

复杂动态网络传播动力学

病毒、谣言、舆论等在现实社会中各种复杂网络系统上的传播现象与人们现代社会经济生活息息相关,对复杂网络上各种传播现象的动力学行为研究是复杂网络理论的一个重要命题.从流行病学的角度, 针对复杂网络传播行为中存在的动态过程,从自适应躲避、响应时滞、离散采样、开放式系统增长等方面综述了复杂网络传播动力学研究的若干进展.      

过去、现在和今后发展

本文评论了钢筋混凝土和预应力混凝土结构有限元分析的过去、现在和今后发展。过去的历史分为二个阶段:1967至1976和1977至1985。本文首先概述了早在1972年就被识别的复杂因素,所需实验工作和今后研究,以及重大事件和重要出版物。然后评论了现况,讨论了这一研究领域中有关课题存在的问题,这些问题在设计工作广泛采用这一研究领域的成果之前就需要解决。最后,在推荐如何把成果应用到设计工作中去以及提出对新的研究领域的认识上,讨论了今后必需的研究与发展重点。     

脑科学中若干非线性动力学问题

对近年发展起来的脑科学中的非线性问题作一介绍.这些问题得到脑科学界的广泛注意.它们是同步, 混沌与混沌周游, 噪声与随机共振.在很多不同背景下的神经生理实验表明脑皮层的振荡活动都存在同步与去同步现象.混沌在人与动物的脑中扮演着重要的角色, 混沌周游与Freeman模型被认为与联想记忆或记忆的动态连接有关.适当噪声强度导致信噪比的极大提高------随机共振是脑神经系统能检测到极其微弱信号的工作机制.噪声也导致同步态并使之稳定.此外,噪声的一些其他作用也在本文中提及.      

混沌网络的聚类同步方法

由于通讯技术和生物工程等科学的发展,许多的聚类同步现象陆续被发 现和研究. 然而,由于网络的复杂性,有关聚类同步的理论结果极少见报道. 从上述问题出发,对于大小为N=mn的网络, 通过构造带权重的相互作用,得 到了n个聚类的聚类同步,其中每个聚类的振子数目都等于m. 最后, 以耦合Lorenz系统为例,验证了以上结果的正确性.     

生物神经网络系统动力学与功能研究

生物神经系统是由数量极其巨大的神经元相互联结的信息网络系统,在生物体的感觉、认知和运动控制中发挥关键性的作用.首先介绍神经元、大脑和一些生物神经网络的生理结构和理论模型,然后分别介绍其放电活动和网络动态特性的一些重要问题,包括神经元的复杂放电模式、耦合神经元网络系统的同步活动和时空动力学、大脑联合皮层神经微回路的网络结构特征,以及工作记忆和抉择过程的动力学机制等. 最后对今后研究给出一些展望.      

神经系统疾病与认知动力学 (I) : 癫痫发作的动力学与控制

研究表明癫痫发作过程与神经系统本身的非线性动力学行为密切相关. 因此, 开展癫痫发作的非线性网络动力学建模与调控问题的研究, 有助于理解癫痫临床表征的动力学机理和定位致痫灶网络, 进而设计有效的网络调控策略. 本文回顾了癫痫脑神经疾病网络动力学与控制方面的研究进展, 系统总结了本文作者近年来在癫痫发作动力学建模分析及其调控等方面取得的研究成果. 首先, 基于海马齿状回CA3区环路神经元网络模型, 分析了影响颞叶癫痫发作的分子和网络结构因素, 阐释了癫痫发作转迁的动力学机制. 其次, 由于脑神经系统的集群编码特性, 基于神经场模型和平均场模型建模方法完善了皮质?基底节?丘脑环路网络动力学理论框架, 并基于此框架分析了失神癫痫发作转迁的动力学分岔机制, 探讨了不同类型癫痫发作的转迁路径, 发现了失神癫痫发作转迁的多稳态共存现象, 揭示了时滞对失神癫痫同步发作的控制效果, 设计了丰富有效的癫痫深脑刺激调控策略, 给出了电刺激调控失神癫痫发作的动力学解释. 最后, 通过数据驱动的统计建模和神经元群模型动力学建模分析, 提出了局灶癫痫致痫灶定位及寻找有效控制癫痫发作网络关键节点的理论新方法. 这些研究成果为理解难治性癫痫发作动力学本质及在临床诊疗的应用方面提供重要理论支撑. 最后对进一步研究给出若干建议.      

同步型爆炸与门研究

介绍了爆轰波相互作用形成正规反射或马赫反射后，在作用点附近波后状态增强的现象，利用该现象研制了同步型爆炸与门，并得到这种与门的有关重要参数。     

Outer synchronization of uncertain small-world networks via adaptive sliding mode control

The outer synchronization of irregular coupled complex networks is investigated with nonidentical topological structures. The switching gain is estimated by an adaptive technique, and a sliding mode controller is designed to satisfy the sliding condition. The outer synchronization between two irregular coupled complex networks with different initial conditions is implemented via the designed controllers with the corresponding parameter update laws. The chaos synchronization of two small-world networks consisting of N uncertain identical Lorenz systems is achieved to demonstrate the applications of the proposed approach.     

Cluster synchronization analysis of complex dynamical networks by input-to-state stability

Cluster synchronization is an interesting issue in complex dynamical networks with community structure. In this paper, we study cluster synchronization of complex networks with non-identical systems by input-to-state stability. Some sufficient conditions that ensure cluster synchronization of complex networks are provided. We show that the cluster synchronization is difficult to achieve if there are some links among different clusters. The analysis is then extended to the case where the outer coupling strengths are adaptive. Finally, numerical simulations are given to validate our theoretical analysis.     

Cluster synchronization and rhythm dynamics in a complex neuronal network with chemical synapses

Cluster synchronization and rhythm dynamics are studied for a complex neuronal network with the small world structure connected by chemical synapses. Cluster synchronization is considered as that in-phase burst synchronization occurs inside each group of the network but diversity may take place among different groups. It is found that both one-cluster and multi-cluster synchronization may exist for chemically excitatory coupled neuronal networks, however, only multi-cluster synchronization can be achieved for chemically inhibitory coupled neuronal networks. The rhythm dynamics of bursting neurons can be described by a quantitative characteristic, the width factor. We also study the effects of coupling schemes, the intrinsic property of neurons and the network topology on the rhythm dynamics of the small world neuronal network. It is shown that the short bursting type is robust with respect to the coupling strength and the coupling scheme. As for the network topology, more links can only change the type of long bursting neurons, and short bursting neurons are also robust to the link numbers.     

Noise-induced outer synchronization between two different complex dynamical networks

In this paper, based on the theory of stochastic differential equations, we study the outer synchronization between two different complex dynamical networks with noise coupling. The theoretical result shows that two different complex networks can achieve generalized outer synchronization only with white-noise-based coupling. Numerical examples further verify the effectiveness and feasibility of the theoretical results. Numerical evidence shows that the synchronization rate is proportional to the noise intensity.     

Impact of node dynamics parameters on topology identification of complex dynamical networks

This paper aims at investigating the topology identification problem of complex dynamical networks with varying node dynamics parameters and fixed inner coupling matrices. In particular, by employing the unified chaotic system as node dynamics, this work further explores the influence of continuously changing node dynamics parameters on topology identification of complex dynamical networks with different coupling strengths. Results show that for sufficiently small or large coupling strengths, the performance of topology identification is not affected by the change of node parameters. Specifically, for small enough coupling strengths, the topological structure can be completely identified regardless of the change of node parameters, while for sufficiently large coupling strengths, the connectivity (presence and absence of connections) cannot be successfully identified. Furthermore, for certain coupling strengths, with the increase of node dynamics parameters, the topology identification varies from completely unidentifiable to partially or event completely identifiable. Therefore, the synchronization-based topology identification depends on node dynamics. Even for the same node dynamical model, different parameters can have a significant impact on identification results. Furthermore, for networks consisting of chaotic oscillators defining node dynamics, small coupling strengths are conducive to topology identification. A broader conclusion is that projective synchronization, rather than just complete synchronization, is an obstacle to the network topology identification. The findings in this paper will add to our understanding of conditions for identifying topologies of complex networks.     

Spatiotemporal chaos synchronization between uncertain complex networks with diverse structures

Spatiotemporal chaos synchronization between uncertain complex networks with diverse structures is investigated. The identification law of unknown parameters and the adaptive law of the configuration matrix element in state equations of network nodes are determined based on stability theory, and the conditions of realizing spatiotemporal chaos synchronization between uncertain complex networks with different structures are discussed and obtained. Further, the Fisher–Kolmogorov system with spatiotemporal chaotic behavior is taken as the nodes of drive and response networks to imitate the experiment. It is found that the synchronization performance between two networks is very stable.     

Synchronization of neutral complex dynamical networks with coupling time-varying delays

In this paper, the synchronization problem for a class of neutral complex dynamical networks with coupling time-varying delays is considered. A delay-dependent synchronization criterion is derived for the synchronization of neutral complex dynamical networks. By the use of a convex representation of the sector-restricted nonlinearity in system dynamics, the stability condition based on the discretized Lyapunov?CKrasovskii functional is obtained via LMI (linear matrix inequality) formulation. The effectiveness of our work is verified through a numerical example and simulation.     

Synchronization in a fractional-order dynamic network with uncertain parameters using an adaptive control strategy

This paper studies synchronization of all nodes in a fractional-order complex dynamic network. An adaptive control strategy for synchronizing a dynamic network is proposed. Based on the Lyapunov stability theory, this paper shows that tracking errors of all nodes in a fractional-order complex network converge to zero. This simple yet practical scheme can be used in many networks such as small-world networks and scale-free networks. Unlike the existing methods which assume the coupling configuration among the nodes of the network with diffusivity, symmetry, balance, or irreducibility, in this case, these assumptions are unnecessary, and the proposed adaptive strategy is more feasible. Two examples are presented to illustrate effectiveness of the proposed method.     

Sliding mode synchronization between uncertain Watts-Strogatz small-world spatiotemporal networks

Based on the topological characteristics of small-world networks, a nonlinear sliding mode controller is designed to minimize the effects of internal parameter uncertainties. To qualify the effects of uncertain parameters in the response networks, some effective recognition rates are designed so as to achieve a steady value in the extremely fast simulation time period. Meanwhile, the Fisher-Kolmogorov and Burgers spatiotemporal chaotic systems are selected as the network nodes for constructing a drive and a response network, respectively. The simulation results confirm that the developed sliding mode could realize the effective synchronization problem between the spatiotemporal networks, and the outer synchronization is still achieved timely even when the connection probability of the small-world networks changes.