海域天然气水合物开采的4C-OBC时移地震动态监测模拟

为了掌控海域天然气水合物开采过程中的地质和环境风险,采用能够同时采集纵波信号和横波信号,并且满足实时性和长期性要求的四分量海底震缆(4C-OBC)技术,对天然气水合物储层进行时移地震动态监测.针对未来天然气水合物商业化开发时的水平井环境,通过射线追踪方法进行正演模拟,对地层模型进行地震照明分析,得到合理的4C-OBC布...     

商品流转过程中动态成本优化控制方法与实践

针对商品进销存流转过程的费用问题 ,提出了建立费用数学模型的方法 ,并对经销过程中的动态成本优化问题给出了一系列算法 ,其中的保本保利模型与算法揭示了经营成本变动与商品在库时间的内在联系 ,为成本的优化控制奠定了基础 ,具有很好的理论研究意义和实际应用价值 .     

教学全面、全员、全程动态管理的理论、方法与模型

对教学管理目前的发展趋势和存在的问题进行了分析 ,提出了全面、全员、全程动态的积累水平、波动水平、阶段实际水平和进步指数的概念 ,给出了可操作性的教学质量全面、全员和全程动态管理的评价方法和模型 .该方法与模型已应用到我们所开发的“通宝育杰学校全面质量管理支持系统”中.     

最大速度恒定的连续Petri网(CCPN)的性质及判定方法

最大速度恒定的连续Ｐｅｔｒｉ网是由Ｄａｖｉｄ等最早提出的一类时延连续Ｐｅｔｒｉ网模型,但并未对其性质给出确切的定义及相应的判定方法。文「１」中给出了ＣＣＰＮ的演变图及其构造算法,本文在给出ＣＣＰＮ以对扬系统进行性能分析。     

面向过程再造的企业战略模拟系统研究

依据博弈论的思路并借助于人工神经网络的机理设计并实现了一个面向过程再造的企业战略模拟系统,该系统既为企业的战略制定与实施初步提供了一种无代价的实验手段,又验证了有关企业过程再造的主要理论假设,弥补了其实证研究的不足.并在一家企业得到了应用.     

非线性离散动态大系统的DISOPE关联预测递阶算法

提出了一种非线性离散动态大系统系统优化与参数估计集成的关联预测递阶算法 .在各子系统模型与实际存在差异的情况下 ,将动态系统系统优化与参数估计集成 ( DISOPE)方法与关联预测法相结合 ,得到一个上级协调 ,下级进行各子系统优化与参数估计集成的双环迭代算法 ,从模型出发通过迭代运算能得到实际系统在存在模型 -实际差异时的真实最优解 .仿真结果表明了算法的有效性和实用性.     

短期生产计划中产品完成期的动态决策

生产计划和决策是一个企业成功运营的关键。在研究了产品完成期的基础上提出了动态甘特图 ,用于短期生产计划中产品完成期的动态决策。该动态甘特图具有形象、直观和易于决策等特点。仿真结果表明了它的有效性。     

Dynamic Latent Class Model Averaging for Online Prediction

We consider the problem of online prediction when it is uncertain what the best prediction model to use is. We develop a method called dynamic latent class model averaging, which combines a state‐space model for the parameters of each of the candidate models of the system with a Markov chain model for the best model. We propose a polychotomous regression model for the transition weights to assume that the probability of a change in time depends on the past through the values of the most recent time periods and spatial correlation among the regions. The evolution of the parameters in each submodel is defined by exponential forgetting. This structure allows the ‘correct’ model to vary over both time and regions. In contrast to existing methods, the proposed model naturally incorporates clustering and prediction analysis in a single unified framework. We develop an efficient Gibbs algorithm for computation, and we demonstrate the value of our framework on simulated experiments and on a real‐world problem: forecasting IBM's corporate revenue. Copyright © 2014 John Wiley & Sons, Ltd.     

Dynamic Model Averaging and CPI Inflation Forecasts: A Comparison between the Euro Area and the United States

The paper forecasts consumer price inflation in the euro area (EA) and in the USA between 1980:Q1 and 2012:Q4 based on a large set of predictors, with dynamic model averaging (DMA) and dynamic model selection (DMS). DMA/DMS allows not solely for coefficients to change over time, but also for changes in the entire forecasting model over time. DMA/DMS provides on average the best inflation forecasts with regard to alternative approaches (such as the random walk). DMS outperforms DMA. These results are robust for different sample periods and for various forecast horizons. The paper highlights common features between the USA and the EA. First, two groups of predictors forecast inflation: temporary fundamentals that have a frequent impact on inflation but only for short time periods; and persistent fundamentals whose switches are less frequent over time. Second, the importance of some variables (particularly international food commodity prices, house prices and oil prices) as predictors for consumer price index inflation increases when such variables experience large shocks. The paper also shows that significant differences prevail in the forecasting models between the USA and the EA. Such differences can be explained by the structure of these respective economies. Copyright © 2015 John Wiley & Sons, Ltd.