The Dynamics of Two-State Public Opinion Propagation On Signed Networks

The formation of public opinion on the network is a hot issue in the field of complex network research, and some classical dynamic models are used to solve this problem. The signed network is a particular form of the complex network, which can adequately describe the amicable and hostile relationships in complex real-world systems. However, the methods for studying the dynamic process of public opinion propagation on signed networks still require to be further discussed. In this paper, the authors pay attention to the influence of negative edges in order to design a two-state public opinion propagation mechanism suitable for signed networks. The authors first set the interaction rules between nodes and the transition rules of node states and then apply the model to synthetic and real-world signed networks. The simulation results show that there is a critical value of the negative edge ratio.When the negative edge ratio exceeds this critical value, the evolutionary result of public opinion will change from a consistent state to a split state. This conclusion is also consistent with the distribution result of opinions within communities in the signed network. Besides, the research on the network structural balance shows that the model makes the network evolve in a more balanced direction.     

A Study on Scale Free Social Network Evolution Model with Degree Exponent < 2

Inspired by real world phenomena, in this paper, the authors present a power law evolutional network model with degree exponent β 2. Combinatorial probabilistic method is applied to the theoretical analysis of the presented model. In the proposed model, each time stamp the number of links among a newly added node and old ones follows Poisson distribution with parameter λ and selection probability p. The authors derive exact analytical relationship between the exponent of the power law β, the parameters λ and p. Simulation result is consistent with the exponent β analytical solution. Both theoretical analysis and simulation results show that the presented network evolution model has two obvious real world social network characteristics, degree exponent β 2 and bending phenomena.     

Game-theoretic approach to power and admission control in hierarchical wireless sensor networks

Power efficiency and link reliability are of great importance in hierarchical wireless sensor networks (HWSNs),especially at the key level,which consists of sensor nodes located only one hop away from the sink node called OHS.The power and admission control problem in HWSNs is comsidered to improve its power efficiency and link reliability.This problem is modeled as a non-cooperative game in which the active OHSs are considered as players.By applying a double-pricing scheme in the definition of OHSs’ utility function,a Nash Equilibrium solution with network properties is derived.Besides,a distributed algorithm is also proposed to show the dynamic processes to achieve Nash Equilibrium.Finally,the simulation results demonstrate the effectiveness of the proposed algorithm.     

Building a Tax Predictive Model Based on the Cloud Neural Network

Tax is very important to the whole country, so a scientific tax predictive model is needed. This paper introduces the theory of the cloud model. On this basis, it presents a cloud neural network, and analyzes the main factors which influence the tax revenue. Then if proposes a tax predictive model based on the cloud neural network. The model combines the strongpoints of the cloud model and the neural network. The experiment and simulation results show the ef-     

Strategic Bidding Model for Power Generation Company Based on Repast Platform

： The bidding strategies of power generations in the market are a dynamic and complex problem. It is difficult to analyze and compute with the traditional mathematical methods, which is conspicuous in the middle or long-term transactions. This paper pro- poses a modelity which is the middle or long-term bidding strategy in two-tiers electricity market that is based on the optimal power flow （OPF）. Uncertainties in the outside world are regarded as the agent （Agent） of ＂external environment.＂ Under this conditions, Agent through environment evaluation judges to select viable strategic. Through learning from experiences and opponent＇s behaviors, Agent guides the purpose of the best production. The adaptability and superiority of this model is tested based on Repast with a standard IEEE-5 bus 6 notes test system.     

Application reliability for communication networks and its analysis method

The network reliability is difficult to be evaluated because of the complex relationship among the network components.It can be quite different for different users running different applications on the same network.This paper proposes a new concept and a model of application reliability.Different from the existing models that ignores the effects of applications,the proposed application reliability model considers the effects of different applications on the network performance and different types of network faults and makes the analysis of network components relationship possible.This paper also provides a method to evaluate the application reliability when the data flow satisfies Markov properties.Finally,a case study is presented to illustrate the proposed network reliability model and the analysis method.     

Cluster based hierarchical resource searching model in P2P network

For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a cluster in P2P network, auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.     

Model algorithm control using neural networks for input delayed nonlinear control system

The performance of the model algorithm control method is partially based on the accuracy of the system’s model. It is difficult to obtain a good model of a nonlinear system, especially when the nonlinearity is high. Neural networks have the ability to "learn"the characteristics of a system through nonlinear mapping to represent nonlinear functions as well as their inverse functions. This paper presents a model algorithm control method using neural networks for nonlinear time delay systems. Two neural networks are used in the control scheme. One neural network is trained as the model of the nonlinear time delay system, and the other one produces the control inputs. The neural networks are combined with the model algorithm control method to control the nonlinear time delay systems. Three examples are used to illustrate the proposed control method. The simulation results show that the proposed control method has a good control performance for nonlinear time delay systems.     

Research on the self-defence electronic jamming decision-makingbased on the discrete dynamic Bayesian network

The manner and conditions of running the decision-making system with self-defense electronic jamming are given. After proposing the scenario of applying discrete dynamic Bayesian network to the decision making with self-defense electronic jamming, a decision-making model with self-defense electronic jamming based on the discrete dynamic Bayesian network is established. Then jamming decision inferences by the aid of the algorithm of discrete dynamic Bayesian network are carried on. The simulating result shows that this method is able to synthesize different targets which are not predominant. In this way, various features at the same time, as well as the same feature appearing at different time complement mutually; in addition, the accuracy and reliability of electronic jamming decision making are enhanced significantly.     

基于区域毁伤的基础设施网络抗毁性评估模型

基础设施网络对国家经济、人民生活和社会安全具有重要意义,为了能够准确迅速地评估基础设施网络面对物理攻击或自然灾害时的抗毁性,提出了基于区域毁伤的基础设施网络抗毁性评估模型。该模型将网络的边均分成虚拟节点,提出加权聚类算法寻找关键区域,使用归一化方法平衡网络规模和网络地理覆盖范围对评估结果的影响,算法复杂度低,运算量小,模型应用范围广,便于多个网络之间或同一网络不同规划方案的抗毁性比较。以智利电力输送网络为例,设计多组实验对评估模型进行检验,并给出可视化结果和分析。     

复杂制造协同物流网络资源规划的不确定性控制优化

复杂制造协同物流网络作为动态开放系统,其资源规划决策易受网络中随机不确定性因素影响而出现刚性敏感和稳定失效现象,此问题属于参数状态和概率分布均未知的完全不确定型问题.针对这种问题, 提出了带鲁棒控制约束的期望值模型, 并借助蒙特卡罗方法对资源规划决策中相关参数进行了不确定性仿真. 算例结果表明,与不添加鲁棒约束的优化模型相比, 该模型方法能有效控制不确定性因素的负影响, 增加资源规划决策的稳定性和连续性,为解决完全不确定型问题提供了新视角.     

A model and an analytical method for conveyor systems in distribution centers

The design and operation of high volume conveyor systems in distribution centers are important due to its high cost, large footprint and critical role in the system. However, there is no analytical model available. In this paper, we study the characteristics of the conveyor sortation system, develop a generic model of a complex conveyor network. We then use the Delay and Stock abstraction to develop an approximate analytical method using sample path analysis and dynamic network flow model. The decision variables include length of accumulation segments and the speeds of conveyor components aforementioned decision variables. This analytical solution provides faster analysis, insights and useful subgradient with respect to the decision variables.     

基于贝叶斯学习的复杂系统研制风险演化分析

大型复杂装备的系统结构和研制流程呈现网络化特征,研究风险演化机理有助于控制风险、降低复杂性.通过系统动态过程建模仿真获取数据样本,运用贝叶斯学习从仿真数据样本中提炼风险演化网络,识别不同风险等级的节点之间存在的关联关系,降低了仅凭经验构建风险网络的主观性.对贝叶斯学习获得的风险网络进行概率推理,在总体高风险等级下计算风险网络节点的风险后验概率分布,进而确定风险演化关键节点和传播链路.最后,通过与复杂网络特征指标评估下的静态特征进行对比分析,研究风险网络动态特征与静态特征的差异性,结果表明网络结构特征和风险传播的动态特征共同决定了风险演化关键节点和传播链路.     

中国风险投资网络结构特性及其演化

根据1999～2018年中国风险投资事件,构建了5年移动时间窗的联合风险投资网络,从复杂网络角度研究中国风险投资网络结构、特性及其动态演化规律.研究发现:在网络关系属性方面,中国风险投资网络规模在不断增大,机构之间网络能力差异性大,不利于网络的均衡发展;在网络结构层次方面,中国风险投资网络存在局部紧密性,并且,随着规模...     

关键基础设施工程网络模体与超家族

基于模体和超家族理念自下而上揭示网络结构特征是复杂网络微观研究中的前沿领域. 辨析若干关键基础设施工程网络的子图(模体)特性和超家族特征, 表明工程网络模体表现为经济、技术和地理等因素约束下, 网络设计和建设预先明确的、非随机的特定结构形式, 依据给定分类规则将工程网络子图分为必要、非必要和特征子图三种形式; 基于已有子图比剖面, 以及研究提出的子图浓度和子图组合机制超家族分类方法, 辨析关键基础设施工程网络的家族特征和分类方法异同. 提供了复杂工程网络结构规划设计的系统方法.     

寡头垄断电力市场重复博弈模型及其内在复杂性

在双寡头垄断电力市场中建立了一个动态的古诺重复博弈模型。该模型在需求函数及成本函数均为非线性的前提下,考虑电力公司收取过网费,并将初始产量区分为确定性和随机性两种情况,研究了电力生产者通过适应性地调整产量,获得最大化收益的重复博弈过程,分析了不同参数组合条件下系统表现出来的分岔、混沌、密度周期性等复杂性态,这种将确定性与随机性相结合的新的分析方法对于电力生产者在实际生产中确定最佳产量具指导意义。     

基于超网络的舆论演化动态模型

针对舆论演化过程中的复杂动力学问题,提出了超网络视觉下的舆论演化动态模型,该模型包括节点的添加、重新连接链路、超边的添加以及节点的老化4个过程,其中节点代表关键词,超边代表关键词所构成的话题。其次,利用非均匀网络的演化机制,分别对该动态演化模型的超度、节点自身关注度与节点间影响力两个因素的超度进行了详细的理论分析,分析结果表明节点超度完全符合幂律分布。最后,通过Matlab仿真模拟,分析了不同参数对节点超度分布变化的影响,并进一步验证了结果是遵循幂律分布的。     

发电市场不同决策规则三寡头博弈模型研究

在三寡头垄断的电力市场中假设生产者分别采用静态预期及有限理性预期规则,建立了一个基于不同决策规则的动态博弈模型.对该模型进行了较详细的理论分析和参数仿真计算,在一定范围内得出了系统出现混沌的参数值,分析了不同参数组合条件下系统表现出来的分岔,混沌和密度周期性等复杂性态.所得结果对于一般寡头电力市场中的生产者制定产量计划具有一定启示作用.