油料消耗神经网络组合预测模型

油料消耗单一预测模型精度不高,难以适应信息化条件下精确保障需要。以单一的神经网络预测模型、时间序列预测模型和灰色预测模型为组合预测的基础,利用神经网络求取3种预测模型的组合预测权重系数,将这3种单一预测模型的预测结果作为神经网络组合预测模型的输入,求得一个新的预测结果。平均相对误差和均方差比表明,神经网络组合预测模型比单一预测模型更为优越。     

FORECASTING SERIES CONTAINING OFFSETTING BREAKS: OLD SCHOOL AND NEW SCHOOL METHODS OF FORECASTING TRANSNATIONAL TERRORISM

Transnational terrorism data are difficult to forecast because they contain an unknown number of structural breaks of unknown functional form. The rise of religious fundamentalism, the demise of the Soviet Union, and the rise of al Qaeda have changed the nature of transnational terrorism. ‘Old School’ forecasting methods simply smooth or difference the data. ‘New School’ methods use estimated break dates to control for regime shifts when forecasting. We compare the various forecasting methods using a Monte Carlo study with data containing different types of breaks. The study's results are used to forecast various types of transnational terrorist incidents.     

On the effectiveness of top‐down strategy for forecasting autoregressive demands

We investigate the relative effectiveness of top‐down versus bottom‐up strategies for forecasting the demand of an item that belongs to a product family. The demand for each item in the family is assumed to follow a first‐order univariate autoregressive process. Under the top‐down strategy, the aggregate demand is forecasted by using the historical data of the family demand. The demand forecast for the items is then derived by proportional allocation of the aggregate forecast. Under the bottom‐up strategy, the demand forecast for each item is directly obtained by using the historical demand data of the particular item. In both strategies, the forecasting technique used is exponential smoothing. We analytically evaluate the condition under which one forecasting strategy is preferred over the other when the lag‐1 autocorrelation of the demand time series for all the items is identical. We show that when the lag‐1 autocorrelation is smaller than or equal to 1/3, the maximum difference in the performance of the two forecasting strategies is only 1%. However, if the lag‐1 autocorrelation of the demand for at least one of the items is greater than 1/3, then the bottom‐up strategy consistently outperforms the top‐down strategy, irrespective of the items' proportion in the family and the coefficient of correlation between the item demands. A simulation study reveals that the analytical findings hold even when the lag‐1 autocorrelation of the demand processes is not identical. © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2007.     

基于重构相空间AD-SVM的短期电力负荷预测

利用重庆市九龙坡区电网2009年7月1日000-10月8日4：00 99 d共2 380个历史电力负荷数据,分析其特点和规律.将构建混沌理论的平均位移（AD）法和支持向量机（SVM）相结合,提出了一种新的短期电力负荷预测模型.通过仿真计算,将结果与神经网络法预测结果进行对比,可得新方法能较好反应数据变化趋势,并且具备较好的拟合能力,能够提高负荷预测精度.在实际短期电力负荷预测中,可优先选用平均位移法与支持向量机相结合的新方法.     

神经网络在雷达装备故障趋势预测中的应用

为了提高预测精度,在雷达装备状态监测与故障趋势预测系统中引入基于指数衰减的神经网络预测模型。通过普通BP网络预测模型引出基于指数衰减的神经网络预测模型,经验证预测精度较高。并介绍了雷达装备状态监测与故障趋势预测系统构成,此系统对于雷达装备的早期故障预测和预防性维修具有重要意义。     

战略预警系统数据集成模型研究

设计了一种数据集成在战略预警系统中的应用模型,模型采用XML语言在.NET环境下用C#语言实现战略预警系统的中间件,该中间件将各个子系统粘合在一起,从而形成战略预警系统,该模型为目前急需建设的战略预警系统提供了一种解决方案。     

模拟训练系统中CGF的号手操作技能建模

针对模拟训练系统对CGF真实性的要求,从号手的操作技能入手,建立可以反映真实训练水平的号手操作技能模型。在分析操作技能相关原理和规律的基础上,采用时间序列分析的方法对其进行预测。采用了指数平滑预测模型,详细分析了其原理,并针对其在实际应用中的缺陷进行了改进。最后,通过两个实际案例验证了模型的可用性和可信性。所建模型反映了号手的操作技能,为模拟训练系统中CGF的真实性研究提供新的思路和方法。     

自行火炮内燃机故障先期预测中的仿真应用

通过对自行火炮内燃机汽缸工作过程进行建模 ,并应用质量损失函数 ,对其未来工作过程进行仿真 ,获得其未来工作参数 ,为对内燃机进行故障先期预测打下基础     

末制导段比例导引法的改进

为了消除比例导引法末制导段的各种干扰因素,抑制法向过载的跳动,基于广义卡尔曼滤波多步估计理论,提出了将拦截导弹直接向瞬时遭遇点导引的末制导导引方法,并进行了计算机仿真.仿真结果表明,该方法可以明显消除或减小末制导段比例导引律的各种干扰因素,抑制法向过载的跳动,改善末制导的弹道特性,减小脱靶量,缩短拦截时间.     

灰色-马尔科夫模型在机场道面使用性能预测中的应用

介绍利用灰色-马尔科夫模型对机场道面使用性能进行预测的基本方法和具体步骤,并给出了工程实例.应用分析表明,该方法能够充分利用机场道面使用中各段历史数据,较好地对机场道面使用性能进行预测,且预测结果比单纯的灰色模型有更高的精度.