基于后验风险确定故障样本量的Bayes方法

针对目前测试性故障样本量的确定方法过于粗糙和试验样本量过大的问题,提出了合理运用信息熵方法对装备系统各单元的测试性先验信息进行信息融合,得到装备系统级测试性试验数据。在此基础上得到测试性指标的先验分布,并进一步通过Bayes后验风险准则确定故障样本量及试验方案。以某型电动舵机系统各模块的试验数据为例,以故障检测率为测试性指标,经过分析和计算,发现运用所提方法得到的故障样本量相比传统方法明显减少,从而减少了试验成本,同时得到的测试性指标相对误差较小,保证了可信度。     

Signal Diffusion Mapping: Optimal Forecasting with Time‐Varying Lags

We introduce a new methodology for forecasting, which we call signal diffusion mapping. Our approach accommodates features of real‐world financial data which have been ignored historically in existing forecasting methodologies. Our method builds upon well‐established and accepted methods from other areas of statistical analysis. We develop and adapt those models for use in forecasting. We also present tests of our model on data in which we demonstrate the efficacy of our approach. Copyright © 2015 John Wiley & Sons, Ltd.     

考虑多因素扰动的项目关键链缓冲区间设置及控制模型

项目规划及执行过程中极易出现不确定性,故需对项目关键链缓冲区间进行动态调整以适应项目任务关系变化.针对项目计划关键链缓冲区间设置及调整控制问题,本文提出了一种考虑多因素扰动的缓冲设置及调整控制联动模型.项目初始缓冲设置充分考虑项目工序安全工期、网络复杂程度及资源紧张程度等因素影响,项目执行过程中将缓冲/资源绩效指数与Bayes估计相结合,通过对Weibull分布参数估计实现缓冲信息的递阶转换,提升对缓冲使用的控制能力,最后通过实例验证所提方法与控制模型的有效性.     

利用朴素贝叶斯模型进行多层网络链接预测

针对多层网络链接预测中层间信息融合的问题，提出了一种利用朴素贝叶斯模型的链接预测方法。该方法结合目标层的邻域信息和辅助层相对于目标层的全局信息进行链接预测。在目标层中，根据节点对的邻域信息，利用朴素贝叶斯模型计算其连接概率；在辅助层中，计算节点对在该层有边或无边时在目标层存在链接的概率。在真实数据和合成数据上的实验结果表明：该算法在正相关和负相关的多层网络中都有很好的预测性能。     

不同理性预期下Stackelberg模型的动态复杂性

构建双寡头参与人分别采取有限理性和天真理性预期的Stackelberg博弈模型,研究市场均衡的稳定性条件及动态复杂性特征.通过理论求解和数值模拟得出结论:参与人在不完全信息和不同理性情况下,Stackelberg模型的参数取值范围决定了动态系统的稳定性、产量分岔、利润分岔、奇怪吸引子、吸引子维数和混沌等;如果参数取值满足一定条件,静态Stackelberg推测变差均衡能够实现;否则,Stackelberg推测变差均衡不稳定,非线性动态经济系统可能会出现周期变化或混沌的现象.     

Privacy Quantification Model Based on the Bayes Conditional Risk in Location-Based Services

The widespread use of Location-Based Services(LBSs),which allows untrusted service providers to collect large quantities of information regarding users’locations,has raised serious privacy concerns.In response to these issues,a variety of LBS Privacy Protection Mechanisms(LPPMs)have been recently proposed.However,evaluating these LPPMs remains problematic because of the absence of a generic adversarial model for most existing privacy metrics.In particular,the relationships between these metrics have not been examined in depth under a common adversarial model,leading to a possible selection of the inappropriate metric,which runs the risk of wrongly evaluating LPPMs.In this paper,we address these issues by proposing a privacy quantification model,which is based on Bayes conditional privacy,to specify a general adversarial model.This model employs a general definition of conditional privacy regarding the adversary’s estimation error to compare the different LBS privacy metrics.Moreover,we present a theoretical analysis for specifying how to connect our metric with other popular LBS privacy metrics.We show that our privacy quantification model permits interpretation and comparison of various popular LBS privacy metrics under a common perspective.Our results contribute to a better understanding of how privacy properties can be measured,as well as to the better selection of the most appropriate metric for any given LBS application.     

基于改进主观Bayes方法的不确定推理模型设计

通过分析主观Bayes方法与专家系统中不确定推理的研究现状,提出了一种加入可信度的主观Bayes方法模型.该方法消除了可信度CF和规则成立的充分性量度LS之间可能存在的不一致性,减少了后验概率对先验概率的依赖,并在森林病虫害专家系统中进行应用实现.     

产生式与判别式分类方法比较研究

分类是数据挖掘领域研究的热点,产生式与判别式是数据挖掘中两种不同的分类模型。产生式模型具有通用性、灵活性及清晰的分层结构,学习得到的模型很容易满足模型解释要求;判别式模型没有明显的对系统中变量的基本分布建模的企图,仅仅对输入到输出之间映射的最优化感兴趣,可以提供更好的分类性能。从准确率、建模时间及渐进误差等方面对产生式与判别式分类方法进行了分析与比较,为研究人员在分类模型的选择上提供了参考。