支撑网络零售的物流服务顾客满意度评价

文章采用层次分析法(AHP)和模糊评价方法,通过建立支撑网络零售的物流服务顾客满意度评价指标体系,对我国网络零售下的物流服务进行评价.     

基于WNN对象分类的供应商持续评价的实证

文章构建了供应商持续评价指标体系.在对小渡神经网络进行了深入研究基础上,探讨了基于小波网络的对象分类算法,并将该算法运用于供应商持续评价过程中;最后,进行了供应商持续评价的实证,结果表明:小波网络的对象分类算法对供应商持续评价有较好的分类效果.     

网络组织联台决策方案实施中的风险及其测度

文章分析了网络组织实施联合决策方案过程中的各种风险,描述了网络组织联合决策方案实施过程中的风险评价模型--Fuzzy-AHP评价模型;构建了风险评价指标体系,并应用Fuzzy-AHP评价模型对一个特定网络组织联合决策方案实施过程中的风险进行了评价.     

基于神经网络的地震灾害经济损失评价模型

对地震灾害造成的损失和破坏程度的评价和测度是进行地震应急和灾后重建决策的重要依据.文章首先运用因子分析的方法选择出地震灾害经济损失的评价指标:然后建立了基于神经网络的地震灾害经济损失评价模型并以收集的数据对网络进行了训练;最后应用训练好的网络对地震灾害经济损失进行了预测,并与回归分析的结果进行了比较验证.验证结果表明,模型有较好的可行性和实用性.     

基于模糊数学及网络DEA模型的绿色供应链评价方法

供应链有助于整合上下游企业之间的关系与资源,使企业核心竞争力集聚,要针对性的实施供应链管理就必须首先客观的评价供应链网络的运作效率.网络DEA模型不仅能够评价绿色供应链的绩效,而且能够反映出绿色供应链内部存在的问题.文章基于模糊数学的方法对网络DEA模型进行改进,能够规避传统DEA模型中双重角色因素的影响及不同投入指标必须无量纲化的问题,从而提高绿色供应链评价结果的准确性及便捷性.     

企业网络能力三角模糊评价——以陕西为例

在对企业知识获取过程进行分析的基础上,界定了企业网络能力的内涵,构建了企业网络能力的测度指标体系.通过对不同方法的比较,引入了三角模糊评价法,并经过设计与发放调查问卷,对陕西高技术企业网络能力进行了实证研究,分析结果为针对性地提高企业网络能力提供理论与实证依据.     

基于贝叶斯网络的高校教师绩效评价方法

提出了采用基于贝叶斯网络推理技术的高校教师绩效评价方法.该方法以现有的绩效评价指标体系为基础,结合贝叶斯网络中多层隐类模型的构建原理,将评价指标体系中的各项指标转化为贝叶斯网络中的结点,并根据各级评价指标之间的逻辑关系将其转化为贝叶斯网络中的联接强度,可得到用于教师绩效评价的贝叶斯网络;将各项指标的评价分值作为证据输入到贝叶斯网络中,便能以概率形式评定参评教师的绩效水平.最后分析了教师绩效评价贝叶斯网络的四种典型应用模式.     

供应链物流客户服务水平的评价

为了科学地评价供应链物流客户服务水平,文章从交易前、交易中和交易后三个维度建立了供应链物流客户服务评价评价指标体系:然后运用DEMATAL方法对众多的观测指标进行了优化和降维.最后,在指标体系基础上,采用网络层次分析方法analytic networks process(ANP)对各评价指标的权重进行计算.     

企业微信营销能力的模糊综合评价

随着信息技术和网络技术的迅猛发展,企业利用微信等社交工具进行低成本营销的方法受到了企业界和学术界众多人士的普遍关注.文章综合利用层次分析法(AHP)和模糊综合评价相结合的方法,从定位、行为和效果三个维度建立了企业微信营销能力的评价指标评价体系,进行了实证分析,并对质性指标进行了量化处理.     

隐性知识交流网络中员工重要性评价模型

为了帮助组织了解员工在隐性知识交流网络中的重要性,文章提出了员工重要性评价模型.首先,对隐性知识交流网络中节点(即员工)出度与节点重要性之间的关系、节点入度与节点重要性之间的关系,知识源与节点重要性之间的关系、中介中心性与节点重要性之间的关系进行了分析.在分析的基础上构建了员工重要性评价模型.最后讨论了模型的求解方法,并通过实验验证了模型的合理性.     

Estimation and Confidence Intervals for Clock Offset in Networks with Bivariate Exponential Delays

There is a large and increasing literature on statistical modeling-based estimation of the offset between two clocks. Recent work has focused on the construction of confidence intervals for offset. However, in most of this work it has been assumed that the network delays that occur during the synchronization process are independent. The network delays are often modeled as independent exponential random variables. Thus, we introduce the use of a bivariate exponential distribution to capture the anticipated correlation between the network delays and derive a maximum likelihood estimator and a confidence interval procedure for the offset parameter. We then illustrate how use of the independent model for network delays can lead to improper inference about the offset parameter.     

A SEMIPARAMETRIC BAYESIAN APPROACH TO NETWORK MODELLING USING DIRICHLET PROCESS PRIOR DISTRIBUTIONS

This paper considers the use of Dirichlet process prior distributions in the statistical analysis of network data. Dirichlet process prior distributions have the advantages of avoiding the parametric specifications for distributions, which are rarely known, and of facilitating a clustering effect, which is often applicable to network nodes. The approach is highlighted for two network models and is conveniently implemented using WinBUGS software.     

Elman神经网络在银行间同业拆借利率分析中的应用

利用Elman网络建立了同业拆借利率的神经网络模型,根据msereg性能函数确定了神经网络的输入层个数,采用不同的算法训练网络,从中选出L-M算法是最为快速和准确的。经所提算法训练过的网络在处理复杂时间序列方面具有很好的学习能力和泛化能力。     

基于社会网络分析的意见领袖网结构

微博中的意见领袖主导了大量的信息流动,通过分析他们之间的关系,可以理解微博平台的传播结构。用两阶聚类分析法对新浪微博中的数据进行分析,识别出具有社交网络特征的意见领袖群体,再用社会网络分析方法将识别出来的群体构建成意见领袖网,分析该网络在中心度、密度等方面的特征。实证分析表明:网络中的成员连接关系紧密度不高,网络中没有直接关系的成员要建立联系的平均距离较近,网络中位于核心位置的成员中心化程度不高。     

基于高斯谱聚类的风险商户聚类分析

随着电子支付的普及,市场涌现出越来越多的第三方支付平台,而当前关于第三方支付平台商户风险方面的研究相对较少。故本文提出基于高斯谱聚类的风险商户聚类方法,首先使用高斯混合模型构建交易-交易群体的双模网络;其次借助网络中信息传递的思想构建“商户-交易群体网络”的双模网络;再次使用双模网络聚类方法中的谱聚类方法同时对网络中的两类节点聚类,对商户节点聚类的结果可区分出不同风险级别的商户,对交易群体节点聚类的结果可以进一步描述风险商户的交易特征;最后本文分别在模拟数据和某第方支付平台的实际数据中验证了模型的有效性。实验结果表明,本文提出的方法不仅可以准确地区分出不同风险级别的商户群体,而且能总结归纳风险商户的交易特征,为风险商户的监管提供参考。     

Predicting pickle harvests using a parametric feedforward neural network

Feedforward networks have demonstrated their ability to model non-linear data. Despite this success, their use as a statistical analysis tool has been limited by the persistent assumption that these networks can only be implemented as non-parametric models. In fact, a feedforward network can be used for parametric modeling, with the result that many of the common parametric testing procedures can be applied to the nonlinear network. In this paper, a feedforward network for predicting the biological growth rate of pickles is developed. Using this network, the parametric nature of the network is demonstrated. Once trained, the network model is tested using standard parametric methods. In order to facilitate this testing, it is first necessary to develop a method for calculating the degrees of freedom for the neural network, and the residual covariance matrix. It is shown that the degrees of freedom is determined by the number of parameters that actually contribute to an output. With this information, the covariance matrix can be created by adapting the error matrix. Using these results, the trained network is tested using a simple F-statistic.     

Software reliability assessment using accelerated testing methods

The use of operational profiles and usage-based testing has received considerable attention recently in the software engineering literature. Testing under the actual operational profile can, however, be expensive, time consuming or even infeasible in situations where the performance of a system is dominated by infrequent but highly critical events. We consider a real application that deals with telecommunications network restoration after network failure caused by cuts in fibre optic cables. We use this application to demonstrate the usefulness of traditional accelerated testing methods to test and estimate software reliability. These methods, which have been extensively used in hardware reliability, have an important role to play in software reliability assessment as well.     

复杂产业网络度中心性研究

近年来,基于图论的网络科学方法在经济分析领域受到较多关注,但如何正确使用有着不同的观点。本文将投入产出经济学与网络科学相结合,视经济系统为一个循环流,形成连接稠密、有向加权及自循环的复杂网络拓扑结构,以需求驱动的后向关联技术网络和供给驱动的前向关联技术网络分别构建入强度、出强度、总强度及综合强度中心性指标,并给出相应的经济解释。基于我国2017年投入产出表的测算结果表明,有向加权网络度中心性有效克服了由威弗组合指数形成的有向无权网络度中心性与关联系数矩阵的变异系数相关性显著的缺点,其测算结果更符合产业部门的经济基础特征,且具有信息熵更大、分辨率更高的特点。研究表明,基于有向加权产业网络可以构造出更加全面和有效的度中心性指标,对关键产业部门的识别能够起到补充和完善作用。     

A deletion/substitution/addition algorithm for classification neural networks,with applications to biomedical data

Neural networks are a popular machine learning tool, particularly in applications such as protein structure prediction; however, overfitting can pose an obstacle to their effective use. Due to the large number of parameters in a typical neural network, one may obtain a network fit that perfectly predicts the learning data, yet fails to generalize to other data sets. One way of reducing the size of the parmeter space is to alter the network topology so that some edges are removed; however it is often not immediately apparent which edges should be eliminated. We propose a data-adaptive method of selecting an optimal network architecture using a deletion/substitution/addition algorithm. Results of this approach to classification are presented on simulated data and the breast cancer data of Wolberg and Mangasarian [1990. Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. 87, 9193–9196].