成败型产品验收试验方案研究

在可靠性定型试验的基础上,利用多层贝叶斯方法确定批产品可靠性指标的先验分布,从而制定出成败型产品可靠性验收试验的一种贝叶斯方案.给出利用定型试验信息确定产品可靠性指标先验分布的方法.这种验收方法充分利用了产品定型试验中的先验信息,在确保有较好验收效果的前提下,与传统的验收试验相比,可以大大减少试验量,从而得到可观的经济效益.     

Bayes 试验分析中验前分布的表示

结合武器系统试验鉴定中的问题，研究Ｂａｙｅｓ方法运用中的验前信息表示问题。文中运用自助（Ｂｏｏｔｓｔｒａｐ）方法和随机加权法确定验前分布。对于多种信息源之下的验前信息，给出了验前分布的融合估计。对当前工程实践中常用的方法及存在的问题，提出了看法和处置方法。     

Nonperiodic preventive maintenance for repairable systems

As a complex system with multiple components usually deteriorates with age, preventive maintenance (PM) is often performed to keep the system functioning in a good state to prolong its effective age. In this study, a nonhomogeneous Poisson process with a power law failure intensity is used to describe the deterioration of a repairable system, and the optimal nonperiodic PM schedule can be determined to minimize the expected total cost per unit time. However, since the determination of such optimal PM policies may involve numerous uncertainties, which typically make the analyses difficult to perform because of the scarcity of data, a Bayesian decision model, which utilizes all available information effectively, is also proposed for determining the optimal PM strategies. A numerical example with a real failure data set is used to illustrate the effectiveness of the proposed approach. The results show that the optimal schedules derived by Bayesian approach are relatively more conservative than that for non‐Bayesian approach because of the uncertainty of the intensity function, and if the intensity function are updated using the collected data set, which indicates more severe deterioration than the prior belief, replacing the entire system instead of frequent PM activities before serious deterioration is suggested. © 2010 Wiley Periodicals, Inc. Naval Research Logistics, 2010     

离散动态贝叶斯网络的递推算法

离散动态贝叶斯网络是对动态系统进行建模和定性推理的有力工具。由于观测证据会随时间增加,直接计算推理算法的公式会变得冗长而且推理速度还会下降。在直接计算推理算法的基础上推导出递推公式,并给出算例验证。递推计算公式简洁,仿真表明递推算法的推理速度较直接计算推理算法有明显提高,因而适合实时在线推理。最后将递推算法应用于航天器的态势感知。     

威布尔分布场合下无失效数据的Bayes分析

通过对分布函数进行变换,使变换后的函数成为凹函数,利用凹函数性质给出了各检测时刻失效概率的Bayes估计,进而得到了产品可靠性指标的估计。最后,通过对实际数据进行计算,验证了方法的稳定性。     

基于贝叶斯网络的反舰导弹身份识别

为了解决反舰导弹身份识别所面临的实时性和信息不确定性问题,提出了采用贝叶斯网络识别反舰导弹的方法。设计了4种贝叶斯网络分类器,分别在导弹仿真数据集和UCI数据集上作了测试,比较了它们各自的分类性能。实验结果表明,朴素贝叶斯网络的分类准确率虽然比其它分类器稍低,但它简单有效,稳健性比其它分类器都好,可用于反舰导弹身份的实时识别。     

基于动态贝叶斯网络的复杂网络抗毁性分析

复杂网络抗毁性是复杂网络在节点或边遇敌攻击后能继续维持基本功能的能力,是衡量军事信息网络鲁棒性和敏捷性的重要指标。针对复杂网络及对作战体系支撑能力的多指标、复杂化和动态演绎特点,在静态分析方法基础上,提出基于动态贝叶斯网络的抗毁性分析方法。建立了复杂网络抗毁性指标体系。构建了基于动态贝叶斯网络的复杂网络抗毁性评估模型,提出确定评估模型参数的方法。仿真验证了方法的可行性和有效性。     

An adaptive forecasting algorithm and inventory policy for products with short life cycles

Products with short life cycles are becoming increasingly common in many industries, such as the personal computer (PC) and mobile phone industries. Traditional forecasting methods and inventory policies can be inappropriate for forecasting demand and managing inventory for a product with a short life cycle because they usually do not take into account the characteristics of the product life cycle. This can result in inaccurate forecasts, high inventory cost, and low service levels. Besides, many forecasting methods require a significant demand history, which is available only after the product has been sold for some time. In this paper, we present an adaptive forecasting algorithm with two characteristics. First, it uses structural knowledge on the product life cycle to model the demand. Second, it combines knowledge on the demand that is available prior to the launch of the product with actual demand data that become available after the introduction of the product to generate and update demand forecasts. Based on the forecasting algorithm, we develop an optimal inventory policy. Since the optimal inventory policy is computationally expensive, we propose three heuristics and show in a numerical study that one of the heuristics generates near‐optimal solutions. The evaluation of our approach is based on demand data from a leading PC manufacturer in the United States, where the forecasting algorithm has been implemented. © 2004 Wiley Periodicals, Inc. Naval Research Logistics, 2004.     

改进的前向信息修补算法及其应用

前向信息修补算法可以对离散动态贝叶斯网络的缺失数据进行预测,该算法只适用于所有观测节点是相互独立的网络,却不能处理观测节点有依赖关系网络的缺失数据。针对该算法的这一缺陷,提出了改进的前向信息修补算法,在分析离散动态贝叶斯网络的缺失数据具有二种基本形式的基础上,推导出了每种形式的相应预测公式。继而构建了用于识别威胁源离散动态贝叶斯网络的模型。仿真实验验证了改进的前向信息修补算法的有效性。     

面向诸元计算精度鉴定的二次抽样试验设计方法

针对诸元计算精度常规试验方法的缺点和不足之处,提出了"二次抽样"的试验设计方法,在一次抽样的基础上,通过对试验结果进行分析,进行第二次有针对性的抽样试验,并采用贝叶斯估计方法,对两次相关联的试验结果进行综合评价。最后采用该方法对某型炮兵指控系统的诸元计算精度进行了检验,证明了该方法的实用性和有效性。与常规试验方法相比,二次抽样法对诸元计算中各变量不同值域的权重进行了重新分配,其计算结果对被试装备的实际性能更具有指向性。