基于系统动力学的企业转型的技术变迁路径分析仿真模型

技术变迁是企业转型的重要途径之一,本文分析了信息化环境下企业转型中技术变迁过程,采用系统动力学方法,构建了技术变迁路径的因果图和系统流图;通过案例仿真实验,获取了影响技术变迁的关键因素,即RD投入和企业信息系统上线率.动态模拟结果表明,这两个因素能够有效提高企业转型的经济转化能力,但其投入量必须控制在合理范围之内.     

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.     

Real‐Time Forecasts of Inflation: The Role of Financial Variables

We present a mixed‐frequency model for daily forecasts of euro area inflation. The model combines a monthly index of core inflation with daily data from financial markets; estimates are carried out with the MIDAS regression approach. The forecasting ability of the model in real time is compared with that of standard VARs and of daily quotes of economic derivatives on euro area inflation. We find that the inclusion of daily variables helps to reduce forecast errors with respect to models that consider only monthly variables. The mixed‐frequency model also displays superior predictive performance with respect to forecasts solely based on economic derivatives. Copyright © 2012 John Wiley & Sons, Ltd.     

Backward‐in‐Time Selection of the Order of Dynamic Regression Prediction Model

We investigate the optimal structure of dynamic regression models used in multivariate time series prediction and propose a scheme to form the lagged variable structure called Backward‐in‐Time Selection (BTS), which takes into account feedback and multicollinearity, often present in multivariate time series. We compare BTS to other known methods, also in conjunction with regularization techniques used for the estimation of model parameters, namely principal components, partial least squares and ridge regression estimation. The predictive efficiency of the different models is assessed by means of Monte Carlo simulations for different settings of feedback and multicollinearity. The results show that BTS has consistently good prediction performance, while other popular methods have varying and often inferior performance. The prediction performance of BTS was also found the best when tested on human electroencephalograms of an epileptic seizure, and for the prediction of returns of indices of world financial markets.Copyright © 2013 John Wiley & Sons, Ltd.     

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.     

基于SHPB试验的高速铁路CRTSⅡ型CA砂浆动态性能

采用分离式霍普金森压杆(Split Hopkinson Pressure Bar,SHPB)试验研究了高速铁路CRTS II型水泥乳化沥青砂浆(CA砂浆)的动态力学性能,并建立了CRTS II型CA砂浆的动态本构关系模型.结果表明:随着应变率的增大,CRTS II型CA砂浆峰值强度逐渐增加,但增加速率随应变率的增大而减小,当应变率从44.17增加至54.79 s-1和从54.79增加至108.47 s-1时,峰值强度分别增加了初始峰值强度的52.28%和7.5%,弹性模量随应变率的变化规律性较差;应变率越大,破坏时的贯通裂纹越多,碎裂程度越大;CRTS II型CA砂浆的比能量吸收随着应变率的增大而增大.所建立的动态本构模型拟合曲线与试验曲线具有较好的一致性.     

具有混合执行器故障的多智能体分布式有限时间自适应协同容错控制

针对多智能体编队系统执行器发生故障时,所引起的参数不确定以及系统瞬态不稳定问题,本文采用径向基函数神经网络(radial basis function neural networks, RBFNNs)对不确定参数(未知函数)进行估计。同时,基于反推技术设计出合理的自适应容错控制器,并通过有限时间理论保证系统实现瞬态稳定。首先,本文采用10个智能体作为被控对象,基于有向通讯拓扑结构理论,构建了非线性多智能体系统模型。其次,基于RBFNNs逼近特性,采用反推技术与动态面技术相结合,设计出合理的容错控制器,补偿多智能体中出现的未知非线性执行器故障,并采用有限时间理论解决系统瞬态不稳定问题。接着,基于Lyapunov稳定性理论分析了控制器的稳定性和快速收敛性。最后,通过两种算例对比,验证了所设计的控制器性能优于传统的反推技术,为工程实践提供了一种有效的研究思路。     

煤层冲击倾向性试验及数值模拟

冲击地压是一种特殊的矿山动力现象,而冲击倾向性是煤岩体发生冲击地压的内在因素和必要条件.对松树镇煤矿主采煤层进行冲击倾向性试验,对冲击能量指数、弹性能量指数和动态破坏时间三个煤层冲击能量指标进行分析,得出煤层无冲击倾向性.但对开采煤层数值模拟,得出深部煤层顶部最大主应力值、顶板下沉较大,需要实施一定的防治措施.这为该矿防治冲击地压提供了科学依据.     

气动侧力对汽车性能影响的研究

本文初步探讨了汽车空气动力学中气动侧力对汽车动力性、燃油经济性和操纵稳定性等性能的影响,建立了参数关系的数学模型.