企业知识管理的多阶段优化模型研究

为成功实践企业知识管理，在知识管理优化模型构建及分析的基础上，指出了企业知识管理决策选择的重要性，解析了影响企业知识管理决策选择的相关因素，并对其进行了研究，进而从知识管理战略和知识型员工的管理两方面提出了相应的知识管理方案。     

概念格的属性约简理论与方法

概念格理论是一种有效的知识表示与知识发现的工具，已被成功应用于许多领域．知识发现的一个重要方面是知识约简．提出了概念格的属性约简理论，进一步扩充了概念格理论．给出了概念格约简的判定定理；引入了形式背景的可辨识属性矩阵，并在此基础上得到了寻找约简的方法；同时，给出了概念格不同类型属性的特征。     

基于结构方程模型的大型工程建设项目关键风险因素及作用机理研究

在分析我国大型工程建设项目风险特征的基础上，构建项目风险识别框架。并通过因子分析识别出自然环境、技术、组织管理、资源管理、分包商管理、环保等6方面是大型工程建设项目的关键风险因素。此外，还研究了大型工程建设项目关键风险因素的作用机理及作用模型，并采用结构方程模型检验。     

面向复杂产品系统创新的知识管理系统模型研究

复杂产品系统创新具有多主体知识联盟共同开发和制造,设计高密集性、产品复杂性、生产不确定性的特点.知识管理系统有利于复杂产品系统创新中知识的共享、利用和扩散.在知识管理系统模型研究成果的基础上,构建了基于过程导向的知识链与创新流相结合,以实现知识战略为目标,技术、文化、组织体系为支撑的知识管理系统.明确知识管理战略、把知识管理融入创新流程、创建知识共享文化和激励机制、运行并不断改进系统等策略有利于KMS的实施.     

基于Logistic的项目型组织知识流动与和谐共生模型

分析项目型组织知识流动的两种模式,针对项目型组织知识冲突与共享悖论,引入和谐管理理论界定了项目型组织知识和谐性概念。在此基础上,将项目型组织知识和谐分为组织与个人知识和谐、个人与个人知识和谐两类,以个人与项目型组织的知识存量变化作为研究这项目型组织和谐性的切入点,基于Logistic模型建立了项目型组织知识和谐共生模型及稳定性算法。最后仿真模拟了组织与个人、个人与个人知识存量变化趋势,提出相应的策略以保证项目型组织的知识和谐共生。     

逆向射流火焰稳定机理的研究

在实验的基础上，较深入地探索了逆向射流的火焰稳定机理。通过试验，建立了火焰稳定极限时主流速度Ｖｍ，逆向射流速度Ｖｊ，逆向射流与主流速度比Ｊ和燃料空气当量比Φ之间的准则关系；证实了临界区的存在及其在火焰稳定中的作用；测量了燃烧时流场中的温度分布状况。从流体力学及燃烧理论的基本概念出发，建立了逆向射流火焰稳定的临界区均匀搅拌反应器模型，并以此为基点，最终得到了与试验结果基本一致的逆向射流的火焰稳定准则     

基于多活性代理的复杂信息系统研究

文中在系统理论指导下分析了信息系统（特别是在激烈对抗环境下,需要多种功能动态运筹发挥作用的复杂信息系统）的特点,提出了基于多活性代理的复杂信息系统研究方法．首先,给出了多活性代理方法的基本描述,初步得到了代理的“活性”表征及丧失的定性定量描述与分析,并从系统功能发挥剖面给出了多活性代理的基本动力学表征;其次,从系统自组织功能剖面给出了活性自组织机理的两集合模型;再次,给出了信息安全与对抗领域多活性代理复杂信息系统构建的功能模型,以及在此模型基础上多活性代理的3层次协商、协调模型;分析了基于Swarm群体结构的多活性代理复杂信息系统的3层次机理模型;最后,通过2个具体实例说明了如何利用多活性代理方法分析信息安全对抗领域复杂信息系统．多活性代理理论的提出将衔接应用基础层和应用层研究,对信息安全与对抗领域复杂信息系统的构建和分析提供基本的研究方法与理论支持．     

基于知识发现创新技术的专家系统新构造

为解决“知识匮乏”这一专家系统中的瓶颈问题，提出了基于数据库与知识库协同机制的综合过程模型KD（D＆K）及其相关的创新技术，进而提出了基于知识发现的专家系统ESKD新构造．作为ESKD的核心知识获取构件KD（D＆K）由KDD＊和KDK＊两部分组成，对基于双库协同机制的KDD＊过程模型和基于双基融合机制的KDK＊过程模型分别做了介绍；给出了ESKD的总体框架；并讨论了ESKD的一些子系统和动态知识库系统；最后，在农业真实数据库上验证了ESKD的有效性和先进性．ESKD有望把专家系统推向新阶段．     

一种基于集总参数的心血管系统仿真模型及心音产生机理

基于流体力学与电气网络的相关基础理论,建立一种基于集总参数的心血管系统仿真模型.该心血管系统仿真模型分为三个子模型:体循环子模型、肺循环子模型及心脏子模型.重点分析了体循环子模型和心脏子模型,给出收缩压、舒张压、射血分数等血流参数和仿真波形图,并对心音产生机理进行了分析.然后在此基础上进行扩展,增加了肺循环、血管、耦合壁等,使其形成一个闭合的循环回路,构成了心血管系统仿真模型.利用状态变量分析法建立该模型的数学表达式,并进行模拟仿真,得出心室心房血容量、心房心室压力、动脉血流量等仿真结果,该结果符合健康心脏的生理状况,并且利用该模型仿真了高血压病态和心衰病态状况,仿真结果与临床表现基本一致,表明本文提出的心血管系统仿真模型具有实际的可行性.     

创新的内涵、外延和经济学意义

创新的内涵指在世界上首次引入新东西、引入新概念、制造新变化，其中，“新”指在结构、功能、原理、性质、方法、过程等方面的、首次的、显著性的变化。创新的外延包括知识创新、技术创新、制度创新等。熊彼特的“创新理论”、技术创新理论和知识创新理论反映了创新经济学研究的不同阶段。创新导致暂时垄断，产生超额创新利润，并从价值和利润决定的角度，将市场经济“一分为二”：遵循“劳动价值论”的普通商品市场，遵循“创新价值论”的创新商品市场。     

国内项目知识管理研究进展

本文通过对国内项目知识管理相关研究文献的筛选、整理与分类，回顾了国内学者对项目知识的含义的探讨；从多个视角归纳分析了国内学者对项目知识管理的不同内容和应用领域的研究现状；并指出对项目知识管理信息化的研究还有很大的发展空间。     

Volatility forecasting with double Markov switching GARCH models

This paper investigates inference and volatility forecasting using a Markov switching heteroscedastic model with a fat‐tailed error distribution to analyze asymmetric effects on both the conditional mean and conditional volatility of financial time series. The motivation for extending the Markov switching GARCH model, previously developed to capture mean asymmetry, is that the switching variable, assumed to be a first‐order Markov process, is unobserved. The proposed model extends this work to incorporate Markov switching in the mean and variance simultaneously. Parameter estimation and inference are performed in a Bayesian framework via a Markov chain Monte Carlo scheme. We compare competing models using Bayesian forecasting in a comparative value‐at‐risk study. The proposed methods are illustrated using both simulations and eight international stock market return series. The results generally favor the proposed double Markov switching GARCH model with an exogenous variable. Copyright © 2008 John Wiley & Sons, Ltd.     

Evaluating volatility dynamics and the forecasting ability of Markov switching models

This paper uses Markov switching models to capture volatility dynamics in exchange rates and to evaluate their forecasting ability. We identify that increased volatilities in four euro‐based exchange rates are due to underlying structural changes. Also, we find that currencies are closely related to each other, especially in high‐volatility periods, where cross‐correlations increase significantly. Using Markov switching Monte Carlo approach we provide evidence in favour of Markov switching models, rejecting random walk hypothesis. Testing in‐sample and out‐of‐sample Markov trading rules based on Dueker and Neely (Journal of Banking and Finance, 2007) we find that using econometric methodology is able to forecast accurately exchange rate movements. When applied to the Euro/US dollar and the euro/British pound daily returns data, the model provides exceptional out‐of‐sample returns. However, when applied to the euro/Brazilian real and the euro/Mexican peso, the model loses power. Higher volatility exercised in the Latin American currencies seems to be a critical factor for this failure. Copyright © 2009 John Wiley & Sons, Ltd.     

Non‐linear forecasts of stock returns

Following recent non‐linear extensions of the present‐value model, this paper examines the out‐of‐sample forecast performance of two parametric and two non‐parametric nonlinear models of stock returns. The parametric models include the standard regime switching and the Markov regime switching, whereas the non‐parametric are the nearest‐neighbour and the artificial neural network models. We focused on the US stock market using annual observations spanning the period 1872–1999. Evaluation of forecasts was based on two criteria, namely forecast accuracy and forecast encompassing. In terms of accuracy, the Markov and the artificial neural network models produce at least as accurate forecasts as the other models. In terms of encompassing, the Markov model outperforms all the others. Overall, both criteria suggest that the Markov regime switching model is the most preferable non‐linear empirical extension of the present‐value model for out‐of‐sample stock return forecasting. Copyright © 2003 John Wiley & Sons, Ltd.     

Analysis of the US business cycle with a Vector‐Markov‐switching model

This paper identifies turning points for the US ‘business cycle’ using information from different time series. The model, a multivariate Markov‐switching model, assumes that each series is characterized by a mixture of two normal distributions (a high and low mean) with the switching from one to the other determined by a common Markov process. The procedure is applied to the series composing the composite coincident indicator in the USA to obtain business cycle turning points. The business cycle chronology is closer to the NBER reference cycle than the turning points obtained from the individual series using a univariate model. The model is also used to forecast the series with some encouraging results. Copyright © 2001 John Wiley & Sons, Ltd.     

Adaptive modelling and forecasting of offshore wind power fluctuations with Markov‐switching autoregressive models

Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime‐switching behaviour with an approach relying on Markov‐switching autoregressive (MSAR) models. An appropriate parameterization of the model coefficients is introduced, along with an adaptive estimation method allowing accommodation of long‐term variations in the process characteristics. The objective criterion to be recursively optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one‐step‐ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill. Copyright © 2010 John Wiley & Sons, Ltd.     

Interprétation chymique de la création et origine corpusculaire de la vie chez Athanasius Kircher

The famous Jesuit father Athanasius Kircher (1602–1680) tried to interpret the Creation of the world and to explain the origin of life in the last book of his geocosmic encyclopedia, Mundus subterraneus (Amsterdam, 1664–1665). His interpretation largely depended on the ‘concept of seeds’ which was derived from the tradition of Renaissance ‘chymical’ (chemical and alchemical) philosophy. The impact of Paracelsianism on his vision of the world is also undeniable. Through this undertaking, Kircher namely developed a corpuscular theory for the spontaneous generation of living beings. The present study examines this theory and its relationship with Kircher's chymical interpretation of the Creation in order to place it in its own intellectual and historical context and will uncover one of its most important sources.     

Forecasting the European Credit Cycle Using Macroeconomic Variables

We question the ability of macroeconomic data to predict risk appetite and ‘flight‐to‐quality’ periods in the European credit market using a model inspired by the Markov switching literature. This model allows for a direct mapping of exogenous variables into state probabilities. We find that various surveys and transformed hard data have a forecasting power. We show that despite its depth, the 2008–2009 crisis should not be regarded as an unusual episode that would have to be modelled by an additional state. Finally, we show that our model outperforms a pure Markov switching model in terms of forecasting accuracy, thus clearly indicating that economic figures are helpful in forecasting the credit cycle. Copyright © 2011 John Wiley & Sons, Ltd.