Trajectory Control of Scale-Free Dynamical Networks with Exogenous Disturbances

In this paper, the trajectory control of multi-agent dynamical systems with exogenous disturbances is studied. Suppose multiple agents composing of a scale-free network topology, the performance of rejecting disturbances for the low degree node and high degree node is analyzed. Firstly, the consensus of multi-agent systems without disturbances is studied by designing a pinning control strategy on a part of agents, where this pinning control can bring multiple agents' states to an expected consensus track. Then, theinfluence of the disturbances is considered by developing disturbance observers, and disturbance observers based control (DOBC) are developed for disturbances generated by an exogenous system to estimate the disturbances. Asymptotical consensus of the multi-agent systems with disturbances under the composite controller can be achieved for scale-free network topology. Finally, by analyzing examples of multi-agent systems with scale-free network topology and exogenous disturbances, the verities of the results are proved. Under the DOBC with the designed parameters, the trajectory convergence of multi-agent systems is researched by pinning two class of the nodes. We have found that it has more stronger robustness to exogenous disturbances for the high degree node pinned than that of the lowdegree node pinned.     

复杂产品族零部件关系网络研究

大批量定制的核心是为客户提供多样化产品,同时减少企业内部多样化,以降低产品成本和提高产品质量．在产品零部件实例库基础上,以复杂网络理论和方法为工具,构建复杂产品零部件关系网络,并对网络结构进行分析．在分析网络入度分布和平均出度关系的基础上,提出了复杂产品零部件关系网络的理论模型,并通过汽轮机产品实例数据库进行了验证研究．     

Stability of weighted spectral distribution in a pseudo tree-like network model

The comparison of networks with different orders strongly depends on the stability analysis of graph features in evolving systems. In this paper, we rigorously investigate the stability of the weighted spectral distribution(i.e., a spectral graph feature) as the network order increases. First, we use deterministic scale-free networks generated by a pseudo treelike model to derive the precise formula of the spectral feature, and then analyze the stability of the spectral feature based on the precise formula. Except for the scale-free feature, the pseudo tree-like model exhibits the hierarchical and small-world structures of complex networks. The stability analysis is useful for the classification of networks with different orders and the similarity analysis of networks that may belong to the same evolving system.     

Integrated systemic inflammatory response syndrome epidemic model in scale-free networks

Based on the scale-free network, an integrated systemic inflammatory response syndrome model with artificial immunity, a feedback mechanism, crowd density and the moving activities of an individual can be built. The effects of these factors on the spreading process are investigated through the model. The research results show that the artificial immunity can reduce the stable infection ratio and enhance the spreading threshold of the system. The feedback mechanism can only reduce the stable infection ratio of system, but cannot affect the spreading threshold of the system. The bigger the crowd density is, the higher the infection ratio of the system is and the smaller the spreading threshold is. In addition, the simulations show that the individual movement can enhance the stable infection ratio of the system only under the condition that the spreading rate is high, however, individual movement will reduce the stable infection ratio of the system.     

Mobile user forecast and power-law acceleration invariance of scale-free networks

This paper studies and predicts the number growth of China's mobile users by using the power-law regression. We find that the number growth of the mobile users follows a power law. Motivated by the data on the evolution of the mobile users, we consider scenarios of self-organization of accelerating growth networks into scale-free structures and propose a directed network model, in which the nodes grow following a power-law acceleration. The expressions for the transient and the stationary average degree distributions are obtained by using the Poisson process. This result shows that the model generates appropriate power-law connectivity distributions. Therefore, we find a power-law acceleration invariance of the scale-free networks. The numerical simulations of the models agree with the analytical results well.     

Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

We investigate the influence of blurred ways on pattern recognition of a Barabasi-Albert scale-free Hopfield neural network （SFHN） with a small amount of errors. Pattern recognition is an important function of information processing in brain. Due to heterogeneous degree of scale-free network, different blurred ways have different influences on pattern recognition with same errors. Simulation shows that among partial recognition, the larger loading ratio （the number of patterns to average degree P/ （k） ） is, the smaller the overlap of SFHN is. The influence of directed （large） way is largest and the directed （small） way is smallest while random way is intermediate between them. Under the ratio of the numbers of stored patterns to the size of the network PIN is less than O. 1 conditions, there are three families curves of the overlap corresponding to directed （small）, random and directed （large） blurred ways of patterns and these curves are not associated with the size of network and the number of patterns. This phenomenon only occurs in the SFHN. These conclusions are benefit for understanding the relation between neural network structure and brain function.     

Multiple-predators-based capture process on complex networks

The predator/prey(capture) problem is a prototype of many network-related applications. We study the capture process on complex networks by considering multiple predators from multiple sources. In our model, some lions start from multiple sources simultaneously to capture the lamb by biased random walks, which are controlled with a free parameterα. We derive the distribution of the lamb's lifetime and the expected lifetime T. Through simulation, we find that the expected lifetime drops substantially with the increasing number of lions. Moreover, we study how the underlying topological structure affects the capture process, and obtain that locating on small-degree nodes is better than on largedegree nodes to prolong the lifetime of the lamb. The dense or homogeneous network structures are against the survival of the lamb. We also discuss how to improve the capture efficiency in our model.     

虚拟社区网络的演化过程研究

模拟了虚拟社区网络的演化过程并研究其拓扑结构.发现虚拟社区网络在演化过程中,节点的加入、边的加入、网络中度分布、节点的度与其加入网络时间的关系、平均度随时间的变化等方面与传统的无标度网络有所不符.根据国内某论坛的实际网络数据统计与分析,提出了虚拟社区网络的演化机理——虚拟社区网络构造算法.仿真结果表明,模拟以互联网论坛为代表的虚拟社区网络时,该模型能够得到与真实网络相符的特性. 关键词： 复杂网络 虚拟社区 无标度网络     

Multi-goal Control of Chaotic Connected Complex Networks

Beam transport network （BTN） with small world （SW） （so-called BTN-SW） and Lorenz chaotic connected network with scale-free （SF） are taken as two typical examples, we proposed a global linear coupling and combined with local error feedback methods in sub-networks to realize multi-goal control method of halo and chaos in two networks above. The simulation results show that the methods above is effective for any chaotic connected networks and has a potential of applications in based-halo-chaos secure communication.     

一门崭新的交叉科学:网络科学(下篇)

在上篇综述里,我们评述了网络科学的发展简史,基本概念和网络分类,以及国内外迄今提出的复杂网络的主要理论模型及其拓扑特性。着重总结了我们"一院二校"开展的国家自然科学基金重点项目的研究进展,上篇的研究成果主要涉及加权网络模型、和谐统一的混合模型和量子信息网络模型及其纳米网络,简单说,我们探索了从宏观网络到微观网络的不同层次的若干特点和结果。在下篇综述里,我们将系统概述5个重要课题的研究进展,从第7章到第11章,课题内容包括:复杂网络的动力学完全同步与网络的拓扑结构之间的关系;网络拓扑结构的对称性破缺导致动力学部分同步;给出判断给定网络部分同步斑图稳定性的方法和一般判据,讨论了由网络的多种拓扑对称导致的部分同步斑图的竞争和选择问题以及李指数谱的简并性质。进一步,分别以具有小世界和无标度拓扑结构的束流输运网络为典型的"混沌复杂网络",提出了实现束晕-混沌的同步和控制的若干方法,特别是实现分区网络的多目标的同步控制问题。同时,基于Vicsek模型和Boid模型,研究了生物体集群动态网络的拓扑结构和动力学方程,提出复杂多智能体网络的协调控制策略。另外,从复杂网络的不同拓扑结构对合作涌现和资源分配的作用角度,评述了三个主要的博弈模型—少数者博弈、囚徒困境和雪堆博弈,研究了复杂网络的群聚系数和网络异质性等结构特性对个体的博弈行为的重要影响。最后第12章,我们简介其它课题的进展,讨论了网络科学面临的挑战,并展望了其应用与发展前景。