基于网络结构熵研究网络演化的一种新方法

为了定量描述网络的发展演化,提出了一种网络结构熵的演化表达形式,为更好研究网络的异质性提供了刻画度量,阐述了利用这种方法研究网络演化的原因及通用性。同时,以Internet AS网络为例。求出了其随时间演化关系,指出这种网络的演化发展的方向及其异质性的变化趋势。     

基于子网络结构属性的网络分割研究

Girvan和Newman等人提出了一种基于“中间状态边”概念的网络分割算法,成为研究网络结构的代表性算法。根据“中间状态边”概念,提出了一种新颖的网络分割算法：该算法通过分析构成子网络的两个结构属性,给出了一种划分子网络的方法。根据计算机生成的随机网络和一些真实世界网络的子网络划分实验表明,该算法具有划分准确、计算量小的优点。     

链路预测的方法与发展综述

链路预测对网络结构特征的演化趋势进行挖掘有着不可磨灭的促进作用。为了对网络的未来结构变化进行预测，学者们提出了许多算法。综述了4类较为常见的链路预测方法，分别是基于节点属性、基于网络拓扑结构、基于机器学习以及基于最大似然的方法，比较了4类预测方法的优劣，并概述了几种常见的衡量链路预测算法精确度标准。最后总结并展望了链路预测的未来研究方向和发展前景。     

基于失匹配负波的大脑功能性网络分析

根据正常人与听力损伤患者的失匹配负波（MMN）数据建立大脑功能性网络,计算该大脑功能性网络的复杂网络统计特性,发现所建立的功能性网络相对于随机网络具有类似无标度特性,而且具有高聚类系数、小特征路径长度的小世界网络特性;另外,还计算了功能性网络的平均度和网络结构熵,结果发现正常人的功能性网络的平均度、聚类系数、结构熵等参数均高于听力损伤患者的相应参数,提示了听力损伤后脑功能网络连接减弱可能是声源分辨能力下降的中枢表现,同时也反映了平均度、聚类系数、结构熵等功能性网络参数可作为反应听力损伤后声源分辨能力下降的诊断标志。     

基于P2P的自组织网络路由算法研究*

针对传统的P2P采用泛洪的信息传输方式,网络带宽开销耗费较大,而结构化P2P覆盖网又难以在开销和效率方面做到较好的权衡。根据网络的动态性,有效地建立起一个可分层的树型自治系统,详细描述了该系统的构建目标和体系结构,并基于P2P计算模式动态构建该模型,给出相应的路由发现和更新算法。在理论及仿真实验的基础上对该路由模型的性能进行了验证。结果表明,该网络是一种可运行于任何环境,不受限于系统规模大小、节点能力强弱、节点出入频率,可通过动态调节保证路由效率的广域分布式系统。     

一种基于分区的移动P2P网络路由算法

校园内无线网络的普遍使用与移动设备性能的快速发展,使移动P2P技术得到了更多的关注。但当前对移动对等网络的研究还缺乏统一而明确的定义,还存在很多未能很好地解决的问题。文中的研究目的是如何有效组织移动节点,进行资源交换。针对校园无线网络环境,提出了一种基于分区模型的移动P2P网络结构。对节点历史行为统计,计算节点性能值。并利用分区网络模型和机会路由的思想提出了一种改进路由算法,实现区域内和区域间路由转发,降低了路由开销,提高了网络性能。     

Pathfinder算法优化研究

Pathfinder算法是复杂网络分析及可视化的重要方法,但现有算法时间复杂度大,难以在大数据环境下广泛应用。提出一种基于Prim算法的Pathfinder优化算法,在求解复杂网络图的最小生成树的过程中,通过距离矩阵计算得到Pathfinder算法的结果图。算法时间复杂度可稳定为O(n2)。实验结果表明,在顶点数为500的稠密网络上,该算法的运行时间有较大的优势。     

复杂网络社区发现中标签传播算法的研究与应用

社区结构是复杂网络的一种重要特性,决定了所在网络具备的功能,从复杂网络中发现存在的社区结构,组织形成更大网络社网已成为当前研究热点。为更加准确地发现社区结构,在众多社区发现算法中,标签传播算法以简单高效和易实现的优点得到广泛应用。笔者研究了复杂网络社区发现中标签传播算法的应用。     

基于复杂网络理论的典型电力电子电路复杂性研究

基于复杂网络对电力电子电路的复杂性进行了研究,指出了复杂网络在电力电子技术中运用的结合点并以三相桥式全控整流电路及三相电压型桥式逆变电路为研究对象,提出用复杂网络的特征参数来分析该电路拓扑特性的方法,并对其鲁棒性及脆弱性进行了分析研究.最后对电力电子电路复杂性研究结果存在的问题进行了分析和总结.     

无标度网络度秩指数的变化范围

实证研究表明,绝大多数复杂网络的结点的度分布服从幂律分布,该幂律分布的幂指数的绝对值(度分布指数)介于2和3之间．然而,至今尚未发现为什么度分布指数介于2和3之间的研究结果．本文证明了度分布指数大于2,从而部分回答了上述问题．为此,本文引进度秩指数,并给出了度秩指数和度分布指数之间的关系．通过对度秩指数与网络结构熵之间的关系的刻画,发现了度秩指数与网络结构熵以及网络规模之间的函数依赖关系,从而最终证明了度秩指数的临界值趋于1,并给出了仿真结果．     

基于GA 的系统分解协调算法Agen t实现

为解决实际工程问题，将Agent的设计思想引入分解协调算法，实现了大系统的分解和生产单元之间的协作，并采用GA算法进行参数协调。改进后的分解协调算法具有较好的适用性。     

信号分解法降低OFDM符号峰均功率比的算法

在正交频分复用(OFDM)系统中,信号的峰均功率比(PAPR)较高,导致发射端信号发生失真,为此提出了一种降低PAPR的算法。详细的介绍了在基带部分通过信号分解的方法将OFDM符号分解为两路信号以降低PAPR值,并验证了在不同的比较门限和分解门限下该方法对PAPR的改善程度。实验仿真结果证明,信号分解法在合理的比较门限和分解门限下能够在基带部分降低3dB~4dB的PAPR值。     

Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.     

A vector partitioning approach to detecting community structure in complex networks

In recent years, the problem of community structure detection has attracted more and more attention and many approaches have been proposed. Recently, Newman pointed out that this issue can be transformed into the problem of constrained maximization of the assignment matrix over possible divisions of a network. He presents further that this maximization process can be written in terms of the eigenspectrum of the “modularity matrix”. On the basis of this work and the vector partition approach in computer science, we propose a kind of multiway division approach for detecting community structure of complex networks. Experimental results indicate that the algorithm works well and is effective at finding both good communities and the appropriate number of communities.     

Sewage flow optimization algorithm for large-scale urban sewer networks based on network community division

By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations, a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the steady-state flow of urban sewer networks is first constructed, consisting of a set of algebraic equations with the structure transportation capacities captured as constraints. Since the sewer networks have no apparent natural hierarchical structure in general, it is very difficult to identify the clustered groups. A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks. By integrating the coupling constraints of the subnetworks, the original problem is separated into N optimization subproblems in accordance with the network decomposition. Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution. Finally, an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.     

一种新型的樽海鞘群算法及其应用

为了解决樽海鞘群算法SSA在寻优过程中存在收敛速度慢、计算精度差等问题,提出一种新型的樽海鞘群算法NSSA。首先分析SSA中樽海鞘在追随领导者过程中的不足,然后借鉴灰狼优化算法中追随头狼的思想来改进樽海鞘追随领导者的方式。在23个基准函数上对NSSA与其他算法进行性能比较,并把该算法应用于图像匹配之中。所有实验结果表明,NSSA具有更好的收敛速度、计算精度和鲁棒性。     

Coordination and control for energy distribution in distributed grid networks: Theory and application to power dispatch problem

This paper presents a new framework considering decentralized energy coordination and generation, and flow control for supply–demand balance in distributed grid networks. Consensus schemes using only local information are employed to produce energy coordination, generation, and flow control signals. For the supply–demand balance, it is required to determine the amount of energy needed at each distributed resource. Also, due to the different generation capacities of each energy resource, coordination of energy flows among distributed energy resources is essentially required. Thus, this paper proposes a new framework which gives decentralized energy coordination scheme, generation, and flow control method considering these constraints based on distributed consensus algorithms. The proposed framework in this paper can be nicely utilized in energy dispatch or energy flow scheduling. Furthermore, it can be applied to various engineering problems including water irrigation systems, traffic networks, and building automation systems since it deals with attributed distribution and resource allocation in large scale distributed systems. Through illustrative examples, the effectiveness of the proposed approaches is illustrated. A possible application to power dispatch problem in the IEEE-14bus is also addressed for more detailed and realistic evaluation.     

A new structure adaptation algorithm for RBF networks and its application

An adaptation algorithm is developed for radial basis function network (RBFN) in this paper. The RBFN is adapted on-line for both model structure and parameters with measurement data. When the RBFN is used to model a non-linear dynamic system, the structure is adapted to model abrupt change of system operating region, while the weights are adapted to model the incipient time varying parameters. Two new algorithms are proposed for adding new centres while the redundant centres are pruned, which is particularly useful for model-based control. The developed algorithm is evaluated by modelling a numerical example and a chemical reactor rig. The performance is compared with a non-adaptive model.     

一种改进的演化算法及其在求解复杂优化问题中的应用*

针对传统演化算法在求解函数优化,特别是多峰函数优化问题中出现的早熟现象以及演化后期收敛速度慢等问题,提出了一种新的反序小生境演化算法。该算法采用小生境反序交叉算子,以进一步增强局部寻优的能力;引入一种并行演化算法机制,加强群体寻优能力;同时,根据定义域划分初始种群,增加初始种群的覆盖面积。通过仿真实验表明,与传统的小生境演化算法相比较,利用该算法求解复杂多峰函数优化问题能够明显提高问题的求解精度和收敛速度,而且能够得到所有的全局最优解,更好地避免了求解问题时的早熟现象,达到了较好的效果。     

A revisit to MacKay algorithm and its application to deep network compression

An iterative procedure introduced in MacKay’s evidence framework is often used for estimating the hyperparameter in empirical Bayes. Together with the use of a particular form of prior, the estimation of the hyperparameter reduces to an automatic relevance determination model, which provides a soft way of pruning model parameters. Despite the effectiveness of this estimation procedure, it has stayed primarily as a heuristic to date and its application to deep neural network has not yet been explored. This paper formally investigates the mathematical nature of this procedure and justifies it as a well-principled algorithm framework, which we call the MacKay algorithm. As an application, we demonstrate its use in deep neural networks, which have typically complicated structure with millions of parameters and can be pruned to reduce the memory requirement and boost computational efficiency. In experiments, we adopt MacKay algorithm to prune the parameters of both simple networks such as LeNet, deep convolution VGG-like networks, and residual netowrks for large image classification task. Experimental results show that the algorithm can compress neural networks to a high level of sparsity with little loss of prediction accuracy, which is comparable with the state-of-the-art.