广义逆指数分布元件的可靠性分析

在II型混合截尾样本下,得到了广义逆指数分布未知参数的最大似然估计。利用最大似然估计的渐近正态性构造了参数的渐近置信区间,运用Lindley's逼近方法和TierneyKadane's逼近方法计算出了参数的Bayes估计。最后,运用Monte-Carlo方法对上述估计方法结果作了模拟比较。     

定数截尾的恒定应力加速寿命试验的优化设计

在指数分布场合下讨论了恒加试验的最优设计问题 ,得到了一系列与步加试验相对应的结果 .这里的最优准则是指正常应力水平下产品对数平均寿命估计的方差的极小化 .     

基于指数分布假设的直接序列扩频系统抗窄带干扰技术研究

在扩频信号加信道噪声近似服从高斯分布条件下,利用均匀DFT滤波器组性质,得到变换域谱线幅度平方服从指数分布的结论。通过假设检验方法,可对直接序列扩频系统接收信号中的窄带干扰进行检测和抑制。理论分析和数值仿真结果表明,算法能有效抑制常见的单音、多音干扰以及窄带高斯干扰。     

运行和修复同时进行的固有可用度定时截尾试验方案

针对如何设计一个试验以完成检验系统固有可用度、平均故障间隔时间和平均修复时间等3个指标参数的问题,将试验分为同时进行的可靠性和维修性定时截尾试验2个项目,假设运行时间和修复时间均服从指数分布,且可靠性和维修性试验方案已知,提出了一种固有可用度定时截尾试验方案．该试验方案通过合理选择累积运行时间和累积修复时间,同时满足检验3个指标参数的承制方和使用方风险,可完成对3个指标参数的检验．     

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。     

弹道导弹关机点状态估计的指数加权递归最小二乘方法

在双星预警条件下,将指数加权递归最小二乘算法应用于目标关机点状态估计问题中。通过引入加权因子对目标助推段运动的局部拟线性特性进行描述,从而在一定程度上克服了一般的线性多项式模型难以准确刻画整个助推段运动的难题。通过对助推段目标动力学特性的分析,考察了目标在垂直射面方向上的运动特性。在此基础上,提出了一种更为准确的助推段运动模型。仿真算例表明,所提出的关机点状态估计方法相对于传统的方法具有一定的优越性。     

具有分布时滞的随机反应扩散细胞神经网络的几乎必然指数稳定性

研究一类具有分布时滞和反应扩散的随机细胞神经网络的稳定性。通过构造Lyapunov泛函,并利用It公式、半鞅收敛定理以及不等式技巧,得到了系统几乎必然指数稳定的充分条件。     

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。     

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.     

Global planning of accelerated degradation tests based on exponential dispersion degradation models

The accelerated degradation test (ADT) is an efficient tool for assessing the lifetime information of highly reliable products. However, conducting an ADT is very expensive. Therefore, how to conduct a cost-constrained ADT plan is a great challenging issue for reliability analysts. By taking the experimental cost into consideration, this paper proposes a semi-analytical procedure to determine the total sample size, testing stress levels, the measurement frequencies, and the number of measurements (within a degradation path) globally under a class of exponential dispersion degradation models. The proposed method is also extended to determine the global planning of a three-level compromise plan. The advantage of the proposed method not only provides better design insights for conducting an ADT plan, but also provides an efficient algorithm to obtain a cost-constrained ADT plan, compared with conventional optimal plans by grid search algorithms.