基于复杂网络控制理论的肿瘤关键基因预测研究

复杂网络控制能够捕获整个网络的状态,使得从海量的蛋白质相互作用数据中找到潜在的肿瘤致病基因成为可能。该文利用复杂网络控制理论探究肿瘤关键基因,对5种癌症相关的蛋白质–蛋白质相互作用网络,通过网络最小控制集方法,选取始终处于最小控制集(minimum dominating set,MDS)的基因作为候选关键基因。利用肿瘤相关的生物通路数据和已被证实的肿瘤基因数据,采用富集分析证明了该方法的有效性。构建网络综合中心性指标,对候选关键基因进行排序。进而针对不同的癌症类型,挑选排在前面的候选基因(非已知重要基因集的基因)作为最终的预测基因,基于网络结构和体细胞突变数据分析,对其作为生物标志物的有效性进行验证。该研究在一定程度上为复杂网络控制理论在生物医学中的应用提供了思路。

复杂动态网络的建模、分析与控制研究综述

对信息领域复杂网络的建模、分析与控制方面的研究进展进行了全面的综述,指出了该领域的研究热点和难点,明确了其中存在的重点问题,并指出了今后有意义的研究方向及其可能的解决办法．

模糊神经网络控制及其在复杂工业系统的应用

工业过程日趋复杂,难以建立精确的数学模型,传统控制有其局限性,采用智能控制已成为当前研究的趋势.本文重点探讨了模糊控制和神经网络控制的特点,指出了在对复杂工业过程进行控制时,它们结合的必要性,并分析了模糊逻辑控制与神经网络控制的结合方式.

基于传播免疫的复杂网络可控性研究

复杂网络控制反映了人类对复杂系统的认识深度和改造能力.最新研究成果基于线性系统控制理论建立了复杂网络可控性的理论架构,能够发现任意拓扑结构的线性时不变复杂网络中控制全部节点状态的最小驱动节点集,但是该模型未考虑免疫节点或失效节点对控制信号传播的阻断.在继承该模型优点的前提下,重新构建了基于传播免疫的复杂网络控制模型.在采用分属于随机免疫和目标免疫两种策略的4个方法确定免疫节点的情况下,分析14个真实网络的可控性.结果表明:如果将网络中度数、介数和紧密度指标较高的节点作为免疫节点,将极大地提高控制复杂网络的难度.从而在一定程度上丰富了以往模型的结论.

Hybrid Synchronization of Fractional-Order Complex Networks Model via Fractional Order Controller

For the purpose of investigating two complex networks' hybrid synchronization,a controller with fractional-order is provided.Regarding hybrid synchronization which includes the outer synchronization and inner synchronization,some hybrid synchronization's sufficient conditions according to the Lyapunov stability theorem and the LaSalle invariance principle are proposed.Theoretical analysis suggests that,only when the state of driving-response networks is outer synchronization and each network is in inner synchronization,two coupled networks' hybrid synchronization under some suitable conditions could be reached.Finally,theoretical results are illustrated and validated with the given numerical simulations.

A novel robust fixed time synchronization of complex network subject to input nonlinearity in the presence of uncertainties and external disturbances

This paper studies the global fixed time synchronization of complex dynamical network,including non-identical nodes with disturbances and uncertainties as well as input nonlinearity.First,a novel fixed time sliding manifold is constructed to achieve the fixed time synchronization of complex dynamical network with disturbances and uncertainties.Second,a novel sliding mode controller is proposed to realize the global fixed time reachability of sliding surfaces.The outstanding feature of the designed control is that the fixed convergence time of both reaching and sliding modes can be adjusted to the desired values in advance by choosing the explicit parameters in the controller,which does not rest upon the initial conditions and the topology of the network.Finally,the effectiveness and validity of the obtained results are demonstrated by corresponding numerical simulations.

Exponential Synchronization of Uncertain Complex Dynamical Networks with Delay Coupling

This paper studies the global exponential synchronization of uncertain complex delayed dynamical networks. The network model considered is general dynamical delay networks with unknown network structure and unknown coupling functions but bounded. Novel delay-dependent linear controllers are designed via the Lyapunov stability theory. Especially, it is shown that the controlled networks are globally exponentially synchronized with a given convergence rate. An example of typical dynamical network of this class, having the Lorenz system at each node, has been used to demonstrate and verify the novel design proposed. And, the numerical simulation results show the effectiveness of proposed synchronization approaches.

Selecting pinning nodes to control complex networked systems

One of the fundamental problems in pinning control of complex networks is selecting appropriate pinning nodes, such that the whole system is controlled. This is particularly useful for complex networks with huge numbers of nodes. Recent research has yielded several pinning node selection strategies, which may be efficient. However, selecting a set of pinning nodes and identifying the nodes that should be selected first remain challenging problems. In this paper, we present a network control strategy based on left Perron vector. For directed networks where nodes have the same in-and out-degrees, there has so far been no effective pinning node selection strategy, but our method can find suitable nodes. Likewise, our method also performs well for undirected networks where the nodes have the same degree. In addition, we can derive the minimum set of pinning nodes and the order in which they should be selected for given coupling strengths. Our proofs of these results depend on the properties of non-negative matrices and M-matrices. Several examples show that this strategy can effectively select appropriate pinning nodes, and that it can achieve better results for both directed and undirected networks.

Chaotification of a Continuous Stable Complex Network via Impulsive Control

A method is proposed to chaotify a class of complex networks via impulsive control, when the orbits of the impulsive systems are confined in a bounded area. Based on computing the largest Lyapunov exponent, theoretical results and algorithmic analysis are given in details. Finally, numerical simulations are presented to illustrate the effectiveness of the method.

Complex Cyber-Physical Networks： From Cybersecurity to Security Control

Complex cyber-physical network refers to a new generatio~ of complex networks whose normal functioning significantly relies on tight interactions between its physical and cyber compo- nents. Many modern critical infrastructures can be appropriately modelled as complex cyber-physical networks. Typical examples of such infrastructures are electrical power grids, WWW, public trans- portation systems, state financial networks, and the Interact. These critical facilities play important roles in ensuring the stability of society as well as the development of economy. Advances in informa- tion and communication technology open opportunities for malicious attackers to launch coordinated attacks on cyber-physical critical facilities in networked infrastructures from any Interact-accessible place. Cybersecurity of complex cyber-physical networks has emerged as a hot topic within this con- text. In practice, it is also very crucial to understand the interplay between the evolution of underlying network structures and the collective dynamics on these complex networks and consequently to design efficient security control strategies to protect the evolution of these networks. In this paper, cybersecu- rity of complex cyber-physical networks is first outlined and then some security enhancing techniques, with particular emphasis on safety communications, attack detection and fault-tolerant control, are suggested. Furthermore, a new class of efficient secure the achievement of desirable pinning synchronization control strategies are proposed for guaranteeing behaviors in complex cyber-physical networks against malicious attacks on nodes. The authors hope that this paper motivates to design enhanced security strategies for complex cyber-physical network systems, to realize resilient and secure critical infrastructures.

SPREADING DYNAMICS OF A DISEASE-AWARENESS SIS MODEL ON COMPLEX NETWORKS

In this paper, to better understand the impact of awareness and the network structure on epidemic transmission, we divide the population into four subpopulations corresponding to different physical states and conscious states, and we first propose a modified disease- awareness model, then verify the global stability of the disease-free and endemic equilib- ria, and finally present numerical simulations to demonstrate the theoretical analysis. By examining the spreading influences of model parameters, we find that the outbreak scale can be effectively controlled through increasing the spread rate of awareness or reducing the rate of awareness loss. That is to say, all sorts of media publicity are meaningful. Meanwhile, we find that infection will be affected by consciousness through the control variable.