管理信息系统综合评价的数学模型

本在建立管理信息系统评价指标体系的基础上,通过建立数学模型,研究了管理信息系统的综合评价问题,并用实例加以说明。     

基于模糊神经网络的MVP评价模型及实证分析

利用模糊数学和神经网络方法建立对运动员进行评价的模糊网络模型,采用NBA流行的各评价指标作为其输入,模糊综合评价结果作为输出。样本数据采用2003~2004赛季NBA各单项50强的常规赛数据,分别用BP网络和RBF网络,建立分析系统,比较结果证明RBF网络仿真效果最好,完全可以实用,该模型也可以用在其它综合评价系统中。     

基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型

网络舆情是社会舆情在网络空间中的映射,体现了社会稳定和谐程度.网络舆情态势分析对于有效预测和把握舆情导向具有重大的意义.文章主要研究构建基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型.首先,根据网络舆情态势演化形成原因和发展规律,建立了网络舆情态势等级评价三级指标体系,提出了基于有序比值方法的网络舆情态势等级评价指标权重的确定方法.然后,构建了基于TOPSIS的网络舆情态势等级模糊多指标综合评价模型.最后,通过魏则西事件验证了该模型的有效性和适用性,并与线性加权综合评价模型进行对比分析,说明了文章模型的优越性.研究结果对政府和相关舆情监管人员及时有效的管理和引导网络舆情具有重要的指导意义与参考价值.     

基于多层网络的中国企业风险投资(CVC)影响力动态评价研究

影响力是对被评价主体能力的综合的动态的反应.企业风险投资(corporate venture capital,CVC)作为联系产业资本与技术创新的重要方式,对促进创业企业发展和激励创新有不可忽视的作用.对其影响力进行动态评价,可以综合反映其动态能力,有利于其他主体对其进行直观评估.文章基于CVC多层社会网络,构建了包括整体网-社群网-自中心网的多维影响力评价模型,并加入时间权重,对时序数据提出基于移动时间窗的动态评价模型.该模型既能对被评价对象在静态时间窗上进行多维度影响力评价,又可以反映动态时间窗中影响力变化趋势及其时间延续性,动态衡量被评价对象的多层次综合影响力.文章采用该模型对中国国资背景的CVC进行了动态影响力评价,验证了所研究在实际应用上的有效性.该方法将多层社会网络与影响力综合评价模型相结合,为动态评价方法研究提供了新的思路,可用于对具有网络特征和时间延续性的对象进行动态综合评价.     

基于SVM-FCE的网络学习评价系统

简单介绍SVM的基本理论的基础上,利用SVM和模糊综合评价方法的理论,建立评价指标体系及模糊综合评价模型,解决了网络教学过程中学生学习效果的评价问题,实验表明,运用SVM和模糊综合评价相结合的方法评价网络学习比其他评价方法更加客观、有效.     

基于层次分析法-直觉模糊贝叶斯网络的作战保障能力评估

对作战保障综合能力提出一种新的评估方法,对定性指标构建直觉模糊贝叶斯网络模型,通过贝叶斯推理得到静态作战保障能力;对定量指标用层次分析法确定指标权重,得到动态后勤作战保障能力.采用直觉模糊贝叶斯网络和层次分析相结合的综合评价模型,并引入决策者的偏好因子得到作战保障能力的综合评价值.通过实例分析,验证了该方法的合理性以及有效性.     

煤业集团供应商选择的可拓方法

结合煤业集团的实际，提出了供应商选择的指标体系。应用可拓学的理论与方法，结合熵理论，建立了基于熵权的可拓综合评价模型。由于在该模型中采用了熵权，从而避免了低层次多因素权重确定的主观性；该模型以综合关联度作为评价准则，避免了评价中的主观性。通过将该模型在平顶山煤业集团供应商选择中进行应用，得出了其最佳的供应商。而且评价过程表明，该方法易于操作和使用。     

网络学术期刊核心竞争力评价研究

主要研究网络学术期刊核心竞争力的评价模型与方法.首先,通过分析网络学术期刊核心竞争力的内涵、特征和表现形式,构建网络学术期刊核心竞争力评价指标体系;然后,针对专家判断的随机性和评价指标本身的模糊性,基于贝叶斯统计规则和模糊理论,建立了网络学术期刊核心竞争力的综合评价模型.最后,通过综合评价模型对网络学术期刊核心竞争力进行实证分析,以说明其有效性和实际应用价值.     

模糊环境下带有平衡条件的投资项目评估与选择决策方法

文章提出在模糊环境下求解带有平衡条件的投资项目评估与选择问题的决策方法。该方法由模糊综合评价系统和项目选择的模糊整数规划模型两部分组成。其中模糊综合评价系统采用三角模糊数来描述决策人对项目的主观评价以及多个评价因素的综合，而模糊整数规划模型则描述了各种不同门类利益之间的平衡。最后以实例说明该方法的应用。     

基于ANP-灰色评价法的布局规划方案综合评价研究

制造企业根据布局规划方法制定的布局规划方案在制定完成后往往没有对方案进行综合评价,且忽略了布局方案实施后企业存在的多种风险因素.针对布局规划方案产生的效果和不确定性风险因素作为评价指标,运用网络层次分析法(ANP)确定指标权重,并引入灰色模糊评价法,通过指标评价信息获得灰色评价矩阵最后进行单值化得到方案综合评估值.最后以某公司齿轮机加工车间布局方案为例验证该方法具有可行性和有效性.     

基于软计算协作技术的智能评审管理系统

基于模糊系统、神经网络、遗传算法和粗糙集等软计算的协作技术，建立了科研项目的立项评审智能管理系统。运用软件工程原理与方法，对该系统及其在科研项目立项评审的应用软件进行计划、开发和维护。实际应用表明了该系统的可行性和有效性，并可推广于其他智能管理系统。     

The application of information and communication technologies in transport

This paper examines the possibilities that are opened today and for the whole decade from the application of Information and Communication Technologies (ICTs), in the field of Transport. The various applications are examined, under the following three headings: operation and management of networks (all modes), information and guidance to the users (of the transport systems), operation and management of freight transport systems.For each of these, a concise and critical review is made of the various technologies that exist today in their final stages of development or at the stage of commercial implementation, and their applications. The review refers to various sub-categories of the above main three.The paper proceeds then to examine the prospects for the future with a medium time horizon of 2010. These prospects show that a number of areas of applications have well established technologies and are secured of commercial viability so that we can predict safely their full scale application in the course of this decade. Examples of such areas of applications are: traffic data information collection and dissemination systems, network control and traffic management strategies, vehicle control and driver assistance, systems for (Electronic or other) fee collection.And specifically for freight: freight resource management; terminal and port information and communication systems, freight and vehicle tracking and tracing, and “front” or “back-office” logistics systems.     

Financial credit-risk evaluation with neural and neurofuzzy systems

Credit-risk evaluation decisions are important for the financial institutions involved due to the high level of risk associated with wrong decisions. The process of making credit-risk evaluation decision is complex and unstructured. Neural networks are known to perform reasonably well compared to alternate methods for this problem. However, a drawback of using neural networks for credit-risk evaluation decision is that once a decision is made, it is extremely difficult to explain the rationale behind that decision. Researchers have developed methods using neural network to extract rules, which are then used to explain the reasoning behind a given neural network output. These rules do not capture the learned knowledge well enough. Neurofuzzy systems have been recently developed utilizing the desirable properties of both fuzzy systems as well as neural networks. These neurofuzzy systems can be used to develop fuzzy rules naturally. In this study, we analyze the beneficial aspects of using both neurofuzzy systems as well as neural networks for credit-risk evaluation decisions.     

Crack Detection in Experimental Data by Means of a Spiking Response Model

In engineering tasks, multiple types of neural networks, such as e.g. feed-forward neural networks (FNN) or radial basis function neural networks (RBFN) are common solution methods for a wide scope of applications [1, 2]. Beside the different kinds of artificial neural networks, spiking neural networks (SNN) represent a continuous development in information processing within the computational units of a net. In the contribution, the Spiking Response Model (SRM) [3], a derivative of the Hodgkin-Huxley model, is utilized, whereas the specific properties of this neuron type are used in order to facilitate the evaluation of uniaxial tension test data sets of carbon reinforced concrete specimens. The evaluation procedure of the experimental data targets the identification of major crack appearance based on the load cell data. For reasons of comparison and standardization, numerous uniaxial experiments are performed within the collaborative research project Carbon Concrete Composite C³. Crack detection is considered as showcase for further development of evaluation methods based on SNNs with focal point to engineering experiments. Essential for the evaluation is the transition of the experimental data (load cell data) to the temporal domain of the neuron. Therefore, rate coding [4] is applied resulting in a pre-synaptic spike train. The actual detection of cracks in the pre-synaptic spike train is carried out by an evaluation neuron. The heuristic approach is validated by multiple examples and presents a sufficient accuracy as well as it retains the temporal information regarding the crack occurrence. A feed-forward network structure containing additional evaluation neurons in combination with extra experimental data (e.g. strain gauge data, load cell data) enables extended information extraction such as determining crack location with respect to the attached strain gauges. (© 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

信息几何及其应用

本文介绍了信息几何的基本内容和最新的一些研究进展,特别是信息几何的理论在神经网络、热力学系统、控制系统以及Birkhoff系统中的应用.     

基于不确定语言的航空信息支援系统能力评估研究

航空信息支援系统在空中力量体系对抗过程中起着兵力倍增器的作用,对航空信息支援系统的能力进行定量评估具有重要的现实意义.在介绍了区间数和语言评估标度的基础上,分别提出了区间数的幂运算法则和语言评估标度转化为区间数的方法.通过将语言评价值转化为[0,1]上的区间数,从而实现了定性指标到定量指标的转换.将航空信息支援系统分解为系统能力矢量、人机交互矢量和人员行为矢量,进而分别建立了三个能力矢量的指标体系.应用区间数特征向量法确定了指标体系中各个指标的区间数权重,利用区间集结算子对三个指标体系中的各个指标进行聚合.提出了区间加权矢量和法,利用该方法对三个能力矢量进行聚合.最后通过实例说明了该方法的有效性.     

Fourth-generation warfare: Jihadist networks and percolation

This paper studies the approach to the fourth-generation warfare (4GW) paradigm from the perspective of physical and mathematical disciplines, through the interdisciplinary bridge offered by the analysis of complex networks. The study is within an emerging multidisciplinary field, Sociophysics, which attempts to apply statistical mechanics and the science of complex systems to predict human social behavior. The fourth-generation warfare concept is reviewed, and the war of the Jihadist Islam against the West will be contextualized as 4GW. The paradigm of complex systems has in diverse branches of science changed how collective phenomena are processed. The jihadist networks phenomenon in particular is appropriate for study from the standpoint of complex networks. We present an empirical study of the 9/11 and 11M networks, implemented from public information, and we give a comparison of both networks from the standpoint of complex networks. Several authors have made use of the phenomenon of percolation in complex physical systems to analyse complex networks, particularly jihadist actions like 9/11. The relationship between jihadist networks and percolation is considered. The percolation concept is reviewed and related to 4GW, and the definition of memetic dimension is introduced.     

具有相型服务分布的排队系统的性能灵敏度分析

具有非线数服务分布的排队网络已被广泛应用于许多领域,如通讯网络和管理系统。本文借助于无穷小说矩阵摄动方法,研究了M/PH/1排队系统的稳态性能灵敏度分析问题,给出了性能灵敏度公式,并表明了稳态性能灵敏度很容易通过系统势能进行计算。同时,给出一种计算势能及性能导数的算法。这个算法可直接用于系统的控制与优化,因为它基于分析系统的一条单一样本轨道。最后提供一个数值例子来表明这个算法的应用。     

On effectiveness of wiretap programs in mapping social networks

Snowball sampling methods are known to be a biased toward highly connected actors and consequently produce core-periphery networks when these may not necessarily be present. This leads to a biased perception of the underlying network which can have negative policy consequences, as in the identification of terrorist networks. When snowball sampling is used, the potential overload of the information collection system is a distinct problem due to the exponential growth of the number of suspects to be monitored. In this paper, we focus on evaluating the effectiveness of a wiretapping program in terms of its ability to map the rapidly evolving networks within a covert organization. By running a series of simulation-based experiments, we are able to evaluate a broad spectrum of information gathering regimes based on a consistent set of criteria. We conclude by proposing a set of information gathering programs that achieve higher effectiveness then snowball sampling, and at a lower cost. Maksim Tsvetovat is an Assistant Professor at the Center for Social Complexity and department of Public and International Affairs at George Mason University, Fairfax, VA. He received his Ph.D. from the Computation, Organizations and Society program in the School of Computer Science, Carnegie Mellon University. His dissertation was centered on use of artificial intelligence techniques such as planning and semantic reasoning as a means of studying behavior and evolution of complex social networks, such as these of terrorist organizations. He received a Master of Science degree from University of Minnesota with a specialization in Artificial Intelligence and design of Multi-Agent Systems, and has also extensively studied organization theory and social science research methods. His research is centered on building high-fidelity simulations of social and organizational systems using concepts from distributed artificial intelligence and multi-agent systems. Other projects focus on social network analysis for mapping of internal corporate networks or study of covert and terrorist orgnaizations. Maksim’s vita and publications can be found on Kathleen M. Carley is a professor in the School of Computer Science at Carnegie Mellon University and the director of the center for Compuational Analysis of Social and Organizational Systems (CASOS) which has over 25 members, both students and research staff. Her research combines cognitive science, social networks and computer science to address complex social and organizational problems. Her specific research areas are dynamic network analysis, computational social and organization theory, adaptation and evolution, text mining, and the impact of telecommunication technologies and policy on communication, information diffusion, disease contagion and response within and among groups particularly in disaster or crisis situations. She and her lab have developed infrastructure tools for analyzing large scale dynamic networks and various multi-agent simulation systems. The infrastructure tools include ORA, a statistical toolkit for analyzing and visualizing multi-dimensional networks. ORA results are organized into reports that meet various needs such as the management report, the mental model report, and the intelligence report. Another tool is AutoMap, a text-mining systems for extracting semantic networks from texts and then cross-classifying them using an organizational ontology into the underlying social, knowledge, resource and task networks. Her simulation models meld multi-agent technology with network dynamics and empirical data. Three of the large-scale multi-agent network models she and the CASOS group have developed in the counter-terrorism area are: BioWar a city-scale dynamic-network agent-based model for understanding the spread of disease and illness due to natural epidemics, chemical spills, and weaponized biological attacks; DyNet a model of the change in covert networks, naturally and in response to attacks, under varying levels of information uncertainty; and RTE a model for examining state failure and the escalation of conflict at the city, state, nation, and international as changes occur within and among red, blue, and green forces. She is the founding co-editor with Al. Wallace of the journal Computational Organization Theory and has co-edited several books and written over 100 articles in the computational organizations and dynamic network area. Her publications can be found at: http://www.casos.cs.cmu.edu/bios/carley/publications.php