The Complex System Modeling Method Based on Uniform Design and Neural Network

In this paper, the method based on uniform design and neural network is proposed to model the complex system. In order to express the system characteristics all round, uniform design method is used to choose the modeling samples and obtain the overall information of the system;for the purpose of modeling the system or its characteristics, the artificial neural network is used to construct the model. Experiment indicates that this method can model the complex system effectively.

Coarse-Graining Method Based on Hierarchical Clustering on Complex Networks

With the rapid development of big data, the scale of realistic networks is increasing continually. In order to reduce the network scale, some coarse-graining methods are proposed to transform large-scale networks into mesoscale networks. In this paper, a new coarse-graining method based on hierarchical clustering (HCCG) on complex networks is proposed. The network nodes are grouped by using the hierarchical clustering method, then updating the weights of edges between clusters extract the coarse-grained networks. A large number of simulation experiments on several typical complex networks show that the HCCG method can effectively reduce the network scale, meanwhile maintaining the synchronizability of the original network well. Furthermore, this method is more suitable for these networks with obvious clustering structure, and we can choose freely the size of the coarse-grained networks in the proposed method.

Ranking important nodes in complex networks by simulated annealing

In this paper, based on simulated annealing a new method to rank important nodes in complex networks is presented.First, the concept of an importance sequence（IS） to describe the relative importance of nodes in complex networks is defined. Then, a measure used to evaluate the reasonability of an IS is designed. By comparing an IS and the measure of its reasonability to a state of complex networks and the energy of the state, respectively, the method finds the ground state of complex networks by simulated annealing. In other words, the method can construct a most reasonable IS. The results of experiments on real and artificial networks show that this ranking method not only is effective but also can be applied to different kinds of complex networks.

SYNCHRONIZATION OF MASTER-SLAVE MARKOVIAN SWITCHING COMPLEX DYNAMICAL NETWORKS WITH TIME-VARYING DELAYS IN NONLINEAR FUNCTION VIA SLIDING MODE CONTROL

In this article, a synchronization problem for master-slave Markovian switching complex dynamical networks with time-varying delays in nonlinear function via sliding mode control is investigated. On the basis of the appropriate Lyapunov-Krasovskii functional, introducing some free weighting matrices, new synchronization criteria are derived in terms of linear matrix inequalities （LMIs）. Then, an integral sliding surface is designed to guarantee synchronization of master-slave Markovian switching complex dynamical networks, and the suitable controller is synthesized to ensure that the trajectory of the closed-loop error system can be driven onto the prescribed sliding mode surface. By using Dynkin＇s formula, we established the stochastic stablity of master-slave system. Finally, numerical example is provided to demonstrate the effectiveness of the obtained theoretical results.

Attractive target wave patterns in complex networks consisting of excitable nodes

This review describes the investigations of oscillatory complex networks consisting of excitable nodes, focusing on the target wave patterns or say the target wave attractors. A method of dominant phase advanced driving （DPAD） is introduced to reveal the dynamic structures in the networks supporting osciUations, such as the oscillation sources and the main excitation propagation paths from the sources to the whole networks. The target center nodes and their drivers are regarded as the key nodes which can completely determine the corresponding target wave patterns. Therefore, the center （say node A） and its driver （say node B） of a target wave can be used as a label, （A, B）, of the given target pattern. The label can give a clue to conveniently retrieve, suppress, and control the target waves. Statistical investigations, both theoretically from the label analysis and numerically from direct simulations of network dynamics, show that there exist huge numbers of target wave attractors in excitable complex networks if the system size is large, and all these attractors can be labeled and easily controlled based on the information given by the labels. The possible applications of the physical ideas and the mathematical methods about multiplicity and labelability of attractors to memory problems of neural networks are briefly discussed.

A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge＇s weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes＇ functions effectively. In order to detect the community structure efficiently, a threshold coefficient t~ to evaluate the equivalence of edges＇ weights and a new weighted modularity based on the weight＇s similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism, it presents a weight＇s agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time O（n2 log~）. Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of t~ the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

物联网环境下复杂光纤通信传输网络节点调度

为了解决物联网环境下,光纤通信传输网络节点能耗高、调度效率低的问题,提出一种物联网环境下复杂光纤通信传输网络节点调度方法。在物联网环境下,分析光纤通信传输网络节点的分布情况,根据动态联盟理论,以最快调度速度和最小投资为目标,构建复杂光纤通信传输网络节点调度模型。通过蚁群和粒子群算法相结合的融合算法展开全部搜索,获取最佳调度方案,实现光纤通信传输网络节点调度。实验结果表明,所提方法的节点平均能耗在6.0 mJ以下,LBF值始终高于0.20,且节点调度耗时始终低于200 ms,具有良好的调度效果。

蒙古语词同现网络和句法网络的比较研究

在计量语言学中,复杂网络方法用于研究语言系统的整体特征。本文以《中华人民共和国民法典》(蒙古文版)为原语料构建了蒙古语词同现网络和依存句法网络,在此基础上,比较研究了两种语言网络的统计参数,包括节点数、边数、平均度数、平均路径长度、密度、聚类系数等。结果表明:(1)词同现网络会造成句子部分信息的缺失;(2)词同现网络中词与词之间是线性关系,依存句法网络中词与词之间受依存语法的限制,从而两种网络的参数存在显著差异;(3)蒙古语作为一种典型的黏着语,其聚集系数小于孤立语和屈折语,平均路径长度大于孤立语和屈折语,该结果验证了前人的研究结论。此外,蒙古语与世界上其他语言一样,具有小世界、无标度和相关性的特征。本文拓展了计量研究领域,以新的视角研究了蒙古语,有助于推动少数民族语言研究的发展。

基于复杂网络的交通序列数据特性

为了进一步研究交通流特性,采用复杂网络方法对交通序列数据进行分析。提出了箱型图-聚类算法模型用于识别和填充初始数据中的缺失值和异常值;通过相空间重构方法将一维数据重构为网络节点,选取连接阈值确定网络节点的连接关系,将交通序列数据构建为复杂网络,对复杂网络的结构和定量指标进行分析。研究结果表明交通序列数据复杂网络的结构一定程度上可以反映路段的交通流状态。该结果有助于优化数据预处理方法,拓展复杂网络在交通序列数据研究中的应用。

变步长仿真与改进熵权法联动的综合能源系统鲁棒性评估方法

综合能源系统作为能源互联网的重要表现形式,在提高能源利用率的同时,由于耦合度过高,各系统之间响应速度差异过大,所以带来了更多风险.从系统安全的角度出发,准确找出系统中的薄弱环节,评估系统的鲁棒性至关重要.为此,在复杂网络环境下,提出变步长仿真与改进熵权法联合的鲁棒性评估方法.介绍综合能源系统的结构并进一步解释系统耦合环节;提出包含网络破损度、联通因子在内的鲁棒性指标,采用根据不同系统响应时间差异的变步长仿真方法,针对仿真结果提出一种改进熵权法,构建更客观的评价方法;通过案例验证了该评价方法的优越性.

一种空铁联运枢纽节点选择与分级的综合方法

空铁联运枢纽节点的选取与发展定位对构建完善合理的空铁运输网络至关重要。为此,文章基于复杂网络、熵权Topsis与系统聚类理论等提出了一种综合性评价方法,即熵权Topsis-Q型聚类法,同时全面考虑城市的运输能力与社会经济发展水平,对我国空铁联运枢纽城市的选择与分级问题进行了系统研究。首先,以36个国家级流通节点城市为研究对象,构建了有向非加权的空铁联运网络,明确并计算出反映城市运输能力的节点中心性指标。其次,进一步考虑城市社会经济发展水平指标,建立了一个三级综合评价指标体系。再次,应用熵权Topsis-Q型聚类分析法进行枢纽城市筛选与分级,最终确定出2个全国性枢纽城市和7个区域性枢纽城市。文章结论与经济、社会发展高度一致,提出的方法可用于不同模式交通枢纽节点分级评价研究。

复杂网络在土壤重金属含量关联分析中的应用

土壤重金属污染是当今社会最为关注的环境问题,对土壤不同重金属含量变化的关联性研究可以为土壤重金属治理提供理论依据。本文构建了一个复杂网络模型来分析土壤中重金属含量的关联性。通过采集实验基地7个区域的210组环境重金属数据,构建同现矩阵和复杂网络模型来分析该实验基地重金属的关联性。实验的结果显示,应用本文的方法,可以较好地描述不同重金属之间变化的潜在联系。

基于改进节点收缩法的复杂网络节点重要性评估

识别网络中的重要节点对研究网络的拓扑结构及功能特性具有重要的实际应用价值。为了更好地挖掘复杂网络中的重要节点,考虑到节点核心位置和网络拓扑结构变化对节点重要性评估的影响,基于节点收缩法提出两类改进的复杂网络重要节点评估方法,并进行仿真实验与比较分析。一方面,k-shell值对节点位置可进行粗粒化评估,将节点的k-shell值与网络中所有k-shell值之和的比作为节点收缩法得到的节点重要度(IMC)系数,提出基于新的节点重要度K-IMC的改进算法;另一方面,利用网络结构熵刻画网络拓扑结构的变化,结合收缩前后网络标准结构熵的变化,提出基于又一新的节点重要度E-IMC的改进算法。在此基础上,对两类改进的重要节点评估算法进行仿真实验,运用SIR模型和鲁棒性测试对算法性能进行评价与分析。实验结果表明,相较于原始节点收缩法,K-IMC算法和E-IMC算法的重要节点排序结果均表现出更高的准确性。其中,E-IMC算法相较K-IMC算法的准确性更高,KIMC算法相较E-IMC算法的运算效率更高。

老年人照料设施火灾灾害链风险分析

为研究致灾因子和承灾体性质对老年人照料设施火灾风险防控的影响,应用灾害链和复杂网络理论,评估基于复杂网络构建的灾害链网络中边的风险度,并确定DEMATEL法能够使复杂网络分析结果更准确。对国内外老年人照料设施火灾进行统计分析,得出老年人照料设施火灾事故中的致灾因子,利用归纳分析法和灾变链式传递理论构建复杂系统灾害链模型,对所构建的灾害网络的边进行风险度评价,最后使用DEMATEL法修正不同地区、时间等因素导致的灾害网络风险度评估所产生的误差,提高灾害网络风险度评估结果的精度。得到老年人照料设施火灾灾害网络中边的风险度,为老年人照料设施火灾防控提供依据的同时,提供对复杂网络分析结果修正的依据。

地铁站域商业分化特征现状与规划建议

地铁站点对城市商业格局产生了不可忽视的影响,地铁站点周边往往吸引了大量的商业设施。以成都地铁1、2号线运营的39个车站为对象,根据地铁站性质、商业类型构成因素特征指标提取和计算,划分轨道交通车站的不同商业空间类型。不同的商业站点类型可以分为城市及区域商业中心站点、特殊功能型站点、一般型站点、地方型站点。不同类型的站点具备不同业态构成特征,在未来地铁站点规划建设过程中,应结合当地用地资源、地铁站点性质、人流量潜力等因地制宜对地铁站点进行分类,提出有针对性的规划建议。

基于CN-FRAM的公共交通设备设施系统运营安全韧性度量

设备设施故障是公共交通系统运营安全事故发生的主要原因,为更好地度量和增强系统的安全韧性,提出融合复杂网络(CN)与功能共振分析方法(FRAM)的CN-FRAM运营安全韧性度量模型,并将系统韧性定义为扰动下系统性能损失与性能基线之比。首先,根据设备设施系统构成和节点功能,建立CN;其次,将FRAM模型嵌入到CN中,以扩展节点和连接,构建CN-FRAM模型;然后,基于CN-FRAM韧性度量模型分析系统组件之间功能变化的聚合,并在量化系统韧性时综合考虑网络整体效益和组件之间的耦合程度;最后,以南京市地铁信号系统为例,验证方法的可行性和有效性。结果表明:该模型可以量化系统破坏-恢复全过程的韧性,计算故障对系统的影响程度,并以韧性值最大化为目标,展现不同修复策略下的韧性表现,从而为确定最佳恢复顺序提供依据。对比现有方法,该方法所确定的最优恢复策略能显著减少系统因故障造成的整体性能损失,从而提高系统的韧性。

Two-point resistance of an m×n resistor network with an arbitrary boundary and its application in RLC network

A rectangular m × n resistor network with an arbitrary boundary is investigated, and a general resistance formula between two nodes on an arbitrary axis is derived by the Recursion-Transform（RT） method, a problem that has never been resolved before, for the Green＇s function technique and the Laplacian matrix approach are inapplicable to it. To have the exact solution of resistance is important but it is difficult to obtain under the condition of arbitrary boundary. Our result is directly expressed in a single summation and mainly composed of characteristic roots, which contain both finite and infinite cases. Further, the current distribution is given explicitly as a byproduct of the method. Our framework can be effectively applied to RLC networks. As an application to the LC network, we find that our formulation leads to the occurrence of resonances at h_1= 1-cosφ_i-sinφ_icotnφ_i. This somewhat curious result suggests the possibility of practical applications of our formulae to resonant circuits.

An evolutionary model of urban bus transport network based on B-space

In this paper, an evolutionary model of bus transport network in B-space is developed. It includes the effect of the overlapping ratio of new route on network performance and overcomes the disadvantage, i.e. lack of economic consideration, in the evolutionary bus transport network model in P-space proposed by Chen et al （2007）. The degree distribution functions are derived by using the mean-field method and the master equation method, separately. The relationship between the new stop ratio of a route, λ, and the error in exponential of degree distribution function from the mean-field method is developed as ASlope= λ/（1 -λ） ＋ ln（1-λ）. Finally, the bus transport networks of Hangzhou and Nanjing are simulated by using this model, and the results show that some characteristic index values of the simulated networks are closer to the empirical data than those from Chen＇s model.

基于K-shell位置和两阶邻居的复杂网络节点重要性评估方法

K-shell分解法能快速识别复杂网络中的关键节点,但是无法辨别同壳层内节点重要性的差异,并且低估了处于网络边缘位置的高度值节点的重要性。针对这两个问题,提出一种基于K-shell位置和两阶邻居的节点重要性评估方法。该方法根据K-shell分解过程中节点移除的顺序细化节点的全局位置信息,然后综合考虑节点的局部拓扑结构信息和全局位置信息,利用两步长内邻居节点的K-shell位置信息度量节点的重要性。在八个真实网络上用传染病模型进行仿真实验,结果表明,所提方法与其他五种相关方法相比能更准确有效地评估并区分节点的重要性。

一种基于降噪自编码神经网络的积雪产品去云方法

目前应用最为广泛的积雪覆盖区域图(SCA)可由中分辨率成像光谱仪(MODIS)获取,常被用于积雪覆盖时空变化的研究中.由于受云遮挡的影响,MODIS积雪产品存在较大区域的数据缺失.为了消除云遮挡的影响,本文构建一种降噪自编码神经网络模型,建立雪粒径与复杂地形、土地覆盖类型之间的复杂的映射关系,实现云下积雪参数的补全,提高积雪产品的覆盖面积.本文选取开都河流域为研究区域,将MODIS反演得到的积雪产品数据与地形地物数据结合,并通过降噪自编码神经网络(Denoising Autoencoder Artificial Neural Network)、极值雪线法相结合的方法来定量地回归补全高山复杂地形下由于云覆盖导致的积雪缺失数据,从而得到无缺失的逐日雪盖数据.其中,降噪自编码神经网络融合多特征数据,建立地形特征与雪粒径数据之间的非线性映射关系,从而来补全云层下的雪粒径数据;极值雪线法主要用来去除低海拔地区误报值,进一步提高雪盖提取精度.采用MODIS积雪产品对去云结果开展精度验证,本文所提出的去云方法的精度超过86%,有效地提高了雪盖提取精度.因此,本文所提的算法可以有效地去除复杂地形区域的云覆盖.