失业风险预警系统研究

基于我国29年的历史数据,采用BP神经网络模型对我国的失业风险预警问题进行研究。结果表明,神经网络方法在失业风险系统中具有优良的预警效果,其对失业风险综合警情值的预测误差小于3％。相对于景气分析预测法、时间序列分析、灰色预测模型以及回归预测模型等技术,神经网络方法不仅具有良好的预测精度,同时还具备较强的容错能力和泛化能力。因此,在构建我国的失业风险预警系统中,神经网络模型应该是一种被优先考虑的方法。     

分簇传感器网络中对密钥管理方案

本文提出一种分簇传感器网络中的基于双变量多项式的对密钥建立方案,无需可信第三方存在,整个网络被划分戍若干个簇,根据节点的可信度选举出簇头,能够保证任意两个簇头节点都可以建立对密钥,任意两个簇内节点也可以建立对密钥。     

预案分析的贝叶斯网络方法

在对预案进行评估分析和执行过程中常会涉及不确定性问题,传统的预案编制工具关键路径法(Critical Path Method,CPM)不具备处理不确定性问题的能力。本文推荐的贝叶斯网络法(Bayesian Networks)因其处理分析不确定性问题的能力已经被广泛应用于一系列的决策支持应用,但对预案评估分析的应用是新颖的。本文介绍了用贝叶斯网络法分析传统关键路径法编制的预案。     

创业团队成员信任对社会网络与企业创新能力关系的影响

本文通过对吉林省112家有效样本企业进行调查,将社会网络细分为内部社会网络和外部社会网络,在创业团队的定义下检验了社会网络与信任对新创企业创新能力的影响。研究结果表明,内外部社会网络都会对企业创新能力产生积极影响,而团队成员之间的信任则作为调节变量影响两者之间的关系。创业团队成员之间的信任越高,越有利于知识、信息的传播与交流,内部社会网络对企业创新能力的积极影响越大;但却易产生"非我发明症"现象,导致外部社会网络对企业创新能力的积极影响越小。     

基于非线性主成分分析的信用评估模型研究

传统的主成分分析（PCA）本质上是一种线性映射算法,无法有效处理非线性关系的数据。本文在分析自联想神经网络（AANN）的基础上,借鉴传统PCA方法中的序数主成分概念,提出了基于顺序自联想神经网络（SAANN）的非线性主成分分析法（NLPCA）。进一步,结合神经网络（NN）和Logisitic模型,以我国上市公司为研究对象,分别构建了基于NLPCA-NN和NLPCA-Logisitic的信用评估模型。实证结果及ROC曲线分析表明,本文构建的NLPCA相比传统的线性PCA方法能有效地实现数据的非线性特征提取与降维,提高模型预测性能。此外,实证结果还表明,在相同PCA方法处理数据的条件下,神经网络模型的信用评估效果要好于Logisitic模型。     

Forecasting performances of three automated modelling techniques during the economic crisis 2007–2009

In this work we consider the forecasting of macroeconomic variables during an economic crisis. The focus is on a specific class of models, the so-called single hidden-layer feed-forward autoregressive neural network models. What makes these models interesting in the present context is the fact that they form a class of universal approximators and may be expected to work well during exceptional periods such as major economic crises. Neural network models are often difficult to estimate, and we follow the idea of White (2006) of transforming the specification and nonlinear estimation problem into a linear model selection and estimation problem. To this end, we employ three automatic modelling devices. One of them is White’s QuickNet, but we also consider Autometrics, which is well known to time series econometricians, and the Marginal Bridge Estimator, which is better known to statisticians. The performances of these three model selectors are compared by looking at the accuracy of the forecasts of the estimated neural network models. We apply the neural network model and the three modelling techniques to monthly industrial production and unemployment series from the G7 countries and the four Scandinavian ones, and focus on forecasting during the economic crisis 2007–2009. The forecast accuracy is measured using the root mean square forecast error. Hypothesis testing is also used to compare the performances of the different techniques.     

The internationalisation from a network perspective: A longitudinal study

This article explores a range of internationalisation options in terms of their adequacy in explaining the patterns of internationalisation undertaken by contemporary organizations. It is argued that there is a life cycle in the internationalisation of firms and that existing explanations of internationalisation cater for some, but not all, elements in this life cycle. The paper then outlines a longitudinal study of the internationalisation of an Australian firm from its early tentative involvement via export in the 1960's to its acquisition by an overseas firm in 2000. This study shows that network embeddedness appears to offer the most comprehensive explanation for the international life cycle of contemporary organizations.     

Study based on ＂Situational Rationality＂ hypothesis for customer market classification model

The traditional market segmentation was based on ＂transcendental rationality＂ or ＂Situational Rationality＂, studies shows that it had disadvantages. This paper states the ＂Situational＂ integrated rationality hypothesis and then comes up with the market segmenting models and classification algorithm basing on this hypothesis. This algorithm combined the Rough Set theory and Neural Networks in application, which overcome the dilemma that caused complicated network structure and long training time by only using Neural Networks and influenced the classification precision caused by noise disturbance by only using Rough Set methods. Finally, the paper did a comparison experiment between the traditional method and the method we came up, the results shows that the model and algorithm has its advantage on every aspects.     

Returns from social capital in open source software networks

Open Source Software projects base their operation on a collaborative structure for knowledge exchange in the form of provision or reception of information, expertise and feedback on the creation of source code. Here, we address the direction of these knowledge flows among projects throughout social networks and their impact on project success. We identify the roles of membership or contribution that individuals play within projects. We found that connections through contributors who bring their knowledge to the project, improve project success, and that connection through members, who transfer their knowledge towards other projects, enhance project success. Finally, we found that ties through shared membership and contributions hamper project success. The analysis of knowledge flows and their impact on project success imply a translation of returns from investment in social capital, where investment takes the shape of knowledge flows and the returns mean the projects’ diffusion over the network.

知识网络中的社会资本及其作用

知识网络的绩效,在一定程度上取决于网络伙伴间社会资本与信任关系的形成。社会资本在知识网络中起着不可忽视的重要作用,其积极作用主要有:促进组织间交互学习;减少交易费用;促进风险分担;形成知识网络的竞争优势,进而增强网络成员的竞争优势;有利于解决知识网络中产生的"囚徒困境"等问题。而其消极作用主要有:易帮派化;易冒进;易产生锁定。