On computation of the steady-state probability distribution of probabilistic Boolean networks with gene perturbation

Given a Probabilistic Boolean Network (PBN), an important problem is to study its steady-state probability distribution for network analysis. In this paper, we present a new perturbation bound of the steady-state probability distribution of PBNs with gene perturbation. The main contribution of our results is that this new bound is established without additional condition required by the existing method. The other contribution of this paper is to propose a fast algorithm based on the special structure of a transition probability matrix of PBNs with gene perturbation to compute its steady-state probability distribution. Experimental results are given to demonstrate the effectiveness of the new bound, and the efficiency of the proposed method.     

具有偏好锥的链式网络DEA模型

对链式网络DEA模型进行推广,将"偏好锥"引入网络DEA模型.针对中间产出重要性以及决策者评价时的偏好,建立带有产出锥和投入锥相应的两阶段生产可能集,对具有"偏好锥"的链式网络DEA模型,证明了决策单元为网络DEA有效的充要条件,给出了网络DEA有效性与各阶段弱DEA有效性的关系.另外,文章结合具体算例说明了偏好锥的变化对效率评价的影响.关于两阶段的模型以及相关结论可以推广到多阶段网络结构.     

Impulsive synchronization of general continuous and discrete-time complex dynamical networks

This paper mainly investigates the impulsive synchronization of a general complex continuous and discrete-time dynamical network. Firstly, for the continuous complex networks, we give a sufficient condition to guarantee its synchronization. When the sufficient condition is not satisfied, the impulsive controllers are utilized, and some novel criteria are derived to guarantee the network synchronization in this case. What is more significant is that the similar work is extended to the discrete-time networks model. Finally, the results are, respectively, illustrated by a continuous network composed with the chaotic Chen oscillators and a discrete-time network consisting of Hénon map. All numerical simulations verify the effectiveness of the theoretical analysis.     

A characterisation of (max,+)-linear queueing systems

The (max,+)-algebra has been successfully applied to many areas of queueing theory, like stability analysis and ergodic theory. These results are mainly based on two ingredients: (1) a (max,+)-linear model of the time dynamic of the system under consideration, and (2) the time-invariance of the structure of the (max,+)-model. Unfortunately, (max,+)-linearity is a purely algebraic concept and it is by no means immediate if a queueing network admits a (max,+)-linear representation satisfying (1) and (2). In this paper we derive the condition a queueing network must meet if it is to have a (max,+)-linear representation. In particular, we study (max,+)-linear systems with time-invariant transition structures. For this class of systems, we find a surprisingly simple necessary and sufficient condition for (max,+)-linearity, based on the flow of customers through the network. This revised version was published online in June 2006 with corrections to the Cover Date.     

链式网络DEA模型

数据包络分析(DEA)是评价决策单元(DMU)相对有效性的一种工具,现已得到广泛的应用.传统的DEA不考虑系统内部结构,而是将系统作为一个"黑箱"来度量效率.针对多阶段网络结构提出一个新的网络DEA模型—链式网络DEA模型.研究网络决策单元的网络DEA有效性及各个阶段的弱DEA有效性之间的关系,给出了网络DEA有效的充分必要条件.若网络决策单元不是网络DEA有效的,根据模型可以指出系统在哪些阶段是无效的.     

On a Mathematical Comparison between Hierarchy and Network with a Classification of Coordination Structures

This paper proposes a mathematical model to compare a network organization with a hierarchical organization. In order to formulate the model, we define a three-dimensional framework of the coordination structure of a network and of other typical coordination structures. In the framework, we can define a network structure by contrasting it with a hierarchy, in terms of the distribution of decision making, which is one of the main features of information processing. Based on this definition, we have developed a mathematical model for evaluating coordination structures. Using this model, we can derive two boundary conditions among the coordination structures with respect to the optimal coordination structure. The boundary conditions help us to understand why an organization changes its coordination structure from a hierarchy to a network and what factors cause this change. They enable us, for example, to find points of structural change where the optimal coordination structure shifts from a hierarchy to a hierarchy with delegation or from a hierarchy with delegation to a network, when the nature of the task changes from routine to non-routine. In conclusion, our framework and model may provide a basis for discussing the processes that occur when coordination structures change between a hierarchy and a network.     

企业研发组织沟通网络实证及优化策略研究

鉴于企业研发组织内部沟通网络对研发组织中高效、低成本地实现技术信息共享的重要性,本文首先介绍了结构洞的测度方法,并对中间中心性方法进行改进;其次分析了某企业研发组织沟通网络的结构洞情况,指出非正式沟通网络和专业沟通网络的相关性;然后通过比较改进的中心度算法、传统的中心度算法与限制度指标的相似性,验证了该算法的有效性;最后提出一种削弱沟通网络中“核心人物”垄断地位的“搭桥”策略。结果发现:改进的中心度算法适用于带权值沟通网络结构洞的测定,验证了该算法加权方法的有效性,并给出通过非正式组织促进技术信息共享的对策     

On the guaranteed throughput and efficiency of closed re-entrant lines

A closed network is said to be “guaranteed efficient” if the throughput converges under all non-idling policies to the capacity of the bottlenecks in the network, as the number of trapped customers increases to infinity. We obtain a necessary condition for guaranteed efficiency of closed re-entrant lines. For balanced two-station systems, this necessary condition is almost sufficient, differing from it only by the strictness of an inequality. This near characterization is obtained by studying a special type of virtual station called “alternating visit virtual station”. These special virtual stations allow us to relate the necessary condition to certain indices arising in heavy traffic studies using a Brownian network approximation, as well as to certain policies proposed as being extremal with respect to the asymptotic loss in the throughput. Using the near characterization of guaranteed efficiency we also answer the often pondered question of whether an open network or its closed counterpart has greater throughput - the answer is that neither can assure a greater guaranteed throughput. This revised version was published online in June 2006 with corrections to the Cover Date.     

Identification of nonlinear system using extreme learning machine based Hammerstein model

In this paper, a new method for nonlinear system identification via extreme learning machine neural network based Hammerstein model (ELM-Hammerstein) is proposed. The ELM-Hammerstein model consists of static ELM neural network followed by a linear dynamic subsystem. The identification of nonlinear system is achieved by determining the structure of ELM-Hammerstein model and estimating its parameters. Lipschitz quotient criterion is adopted to determine the structure of ELM-Hammerstein model from input–output data. A generalized ELM algorithm is proposed to estimate the parameters of ELM-Hammerstein model, where the parameters of linear dynamic part and the output weights of ELM neural network are estimated simultaneously. The proposed method can obtain more accurate identification results with less computation complexity. Three simulation examples demonstrate its effectiveness.     

Community structure in large-scale cortical networks during motor acts

The purpose of the present work is to evaluate the community structure of the cortical network subserving the neurophysiologic processes in simple motor acts.To this end, we studied the topological properties of the functional brain connectivity in the frequency domain. The functional networks were estimated by means of the imaginary coherence from a dataset of high-resolution EEG recordings (4094 cortical sources) in a group of healthy subjects (n = 10) during a finger extension task. The analysis of the community structure was addressed through a particular detection algorithm that optimizes the modularity, a function related to the level of internal clustering inside the communities in the network. The principal results indicate that the cortical network changes its structural organization during the motor execution with respect to a baseline condition. Notably in the Beta band (12.5–30 Hz), the level of intra-module connectivity decreases, while inter-module connectivity increases reflecting the need for a neural integration of distant regions. Notably, this distributed interaction involves anatomical regions belonging to both the hemispheres including pre-motor and primary motor areas in the frontal and central part of the cortex as well as parietal associative regions, which are related to the planning, selection and execution of actions.     

Network structure,perception level,and participants' welfares

Based on a market consisting of one monopoly and several customers who are embedded in an economic network, we study how the different perception levels about the network structure affect the two kinds of participants' welfares, and then provide some good strategies for the monopoly to mine the information of the network structure. The above question is the embodiment of the “complex structure and its corresponding functions” question often mentioned in the field of complexity science. We apply a two‐stage game to solve for the optimal pricing and consumption at different perception levels of the monopoly and further utilize simulation analysis to explore the influence patterns. We also discuss how this theoretic model can be applied to a real world problem by introducing the statistical exponential random graph model and its estimation method. Further, the main findings have specific policy implications on uncovering network information and demonstrate that it is possible for the policy‐maker to design some win–win mechanisms for uplifting both the monopoly's profit and the whole customers' welfare at the same time. © 2014 Wiley Periodicals, Inc. Complexity 21: 349–362, 2015     

神经网络在轴承故障诊断中的应用

本文以轴承的故障诊断为例用神经网络理论在此方面进行了探索。即通过轴承的频谱信号对一个反向繁殖神经网络的识别功能进行确认，包括一个三层的神经网络模型和用于故障识别的六个特征频谱带设置，所训练的网络可对工业轴承常见的故障进行诊断。实验结果表明神经网络在机械故障振动分析方面无疑是一条走出困境的可行路径。     

Cucker–Smale flocking under rooted leadership and time-varying heterogeneous delays

In this paper, a Cucker–Smale model with time-varying heterogeneous delays in the communication process is investigated, where the delay on each communication link is time-varying and independent of other communication links. A general update rule based on the distance between agents is used to determine the edge weights of the communication network. The method of constructing augmented system is used so that the system stability is transformed into a product convergence problem of time-varying sub-stochastic matrices equivalently. By analyzing this convergence problem, a sufficient condition is established to achieve flocking, which relates to the initial states, the structure of communication network and the upper bound of time delays.     

New results on computable efficiency and its stability for complex networks

The concept of efficiency in a complex dynamic network plays the role of measuring its ability for the exchange of information and its response for the spread of perturbations in diverse applications. This concept is strongly related to the topological properties of the network. A new framework for the definition of network efficiency is discussed, and some of its consequences are established. We also consider how the efficiency changes as the network structure evolves, by introducing the concept of stability associated to the efficiency of a network as an accurate tool to measure the evolution of a dynamic complex network.     

基于正交试验的神经网络设计

神经网络具有固有的优点和广泛的应用,但因其结构设计至今没有确定可寻的方法,使其发展受到很大的影响.为此,提出了基于正交试验的神经网络设计方法.经实证分析,该方法可从任意网络开始,利用正交试验原理改进网络结构,提高网络性能,直至最终设计出较优(或最优)的神经网络;具有原理简单,计算简便,易于应用等特点.     

Measuring centrality by a generalization of degree

Network analysis has emerged as a key technique in communication studies, economics, geography, history and sociology, among others. A fundamental issue is how to identify key nodes, for which purpose a number of centrality measures have been developed. This paper proposes a new parametric family of centrality measures called generalized degree. It is based on the idea that a relationship to a more interconnected node contributes to centrality in a greater extent than a connection to a less central one. Generalized degree improves on degree by redistributing its sum over the network with the consideration of the global structure. Application of the measure is supported by a set of basic properties. A sufficient condition is given for generalized degree to be rank monotonic, excluding counter-intuitive changes in the centrality ranking after certain modifications of the network. The measure has a graph interpretation and can be calculated iteratively. Generalized degree is recommended to apply besides degree since it preserves most favourable attributes of degree, but better reflects the role of the nodes in the network and has an increased ability to distinguish among their importance.     

企业创新网络中跨组织知识共享行为的演化博弈研究

新时期企业创新网络中的跨组织知识共享,成为组织间知识资源有效利用的重要方式。通过构建有限理性、知识位势条件下主客体组织的跨组织知识共享演化博弈模型,研究分析了创新网络中主客体组织跨组织知识共享的策略选择。结果表明企业跨组织知识共享中的合作意愿、合作利益分配、合作双方的知识存量及知识结构差异、知识保护力度、社会联系等均是影响双方合作效率的重要因素。其中,最优的利益分配范围、主客体组织的知识存量及知识结构、知识保护力度等对企业创新网络中跨组织知识共享合作的成功起到关键性作用。     

基于时间序列的网络安全态势预测方法的研究

互联网上每天充斥着大量入侵与攻击事件,已经严重威胁到网络安全,如何把握网络目前安全态势并预测其发展趋势已成为国内外网络应急响应的热点研究内容之一.根据网络安全事件发生的统计规律性,分别研究了基于时间序列的神经网络和灰色系统理论对未来一段时间内的安全事件数量进行预测的方法,并在此基础上对未来的安全态势进行评估,从而得知网络的整体安全运行状况,有效指导了管理员对网络安全的控制.     

BP神经网络非线性组合预测模型在海洋冰情预测中的应用

针对海洋冰情灾害的非线性复杂问题，目前已提出了多种模型对其进行预测。在此基础上，根据神经网络的非线性和良好的函数逼近特性，提出用基于BP神经网络的非线性组合预测（NN-NLCF）模型来预测海洋冰情灾害。结果表明，NN—NLCF模型与海洋冰情的非线性特性相契合，它综合利用了参与组合的多种预测模型的有效信息，因而能更客观地反映海洋冰情的发展趋势，预测结果更为稳健、精度更高，在其它自然灾害时序预测中具有一定的推广应用价值。     

轴辐式同城快递网络模式研究

随着快递网点密度的稠密化,网络结构设计优劣直接关系到快递公司的运营成本和服务水平。针对快递公司的同城快递市场,在不改变现有网点规模选址的基础上改变网点的从属,结合轴辐式网络结构模式设计来提升其时效并优化成本和资源投入。以运输成本最小为目标,建立了带分支流向约束的枢纽选址模型,设计了高效的禁忌搜索算法对问题求解并验证了算法的有效性。最后提供相应的集散点选址分配解决方案,有利于整合资源形成规模效应,同时提供了同城快递分区管理依据,避免因网络结构复杂引起管理和运营混乱;从长远来看,有利于节约运营成本,增加其快递网络的柔性,降低运作管理的难度。