基于改进得分函数的直觉模糊多属性决策方法

针对属性权重信息完全未知的直觉模糊多属性决策问题进行了探究,提出了一种综合考虑隶属度、非隶属度以及犹豫度的新的得分函数,利用改进的得分函数将传统的熵权法推广到直觉模糊领域,为属性权重完全未知的直觉模糊多属性决策问题提供了一个新的排序方法。实际的铁路冻害整治算例进一步说明该方法的可行性和有效性。     

从绍兴文理学院学生五项素质追踪研究看当代大学生的身体素质

通过绍兴文理学院97级学生1026人两次的五项达标成绩的追踪研究,发现五个项目不同分数段的敏感性存在着一定的差异,大学生的体质提高与体育教育有密切关系,同时也与五项素质达标有一定的相关性.     

旅平河南赈灾会活动始末

2 0世纪 2 0年代后期 ,河南发生严重旱灾 ,旅平河南赈灾会组织了颇具规模的灾民移垦活动 ,他们经过比较周密的部署 ,将数万嗷嗷待哺的河南灾民移送到了东北 ,并尽可能地使灾民得到较为妥善的安置。旅平河南赈灾会的此次灾民救助活动在民国河南灾荒救济史上留下了光辉的一页。     

Chan-Vese图像分割模型的快速实现算法的研究

重初始化是水平集方法耗时的一个主要原因,通过将水平集函数与符号距离函数的偏差作为能量项引入C-V模型,以此来约束水平集函数成为符号距离函数,从而去掉了重初始化步骤。在检测多目标时,采用了曲线族代替单曲线作为初始曲线。在数值算法上,利用加性可操作分裂算子(AOS)消除了对时间步长的限制,可以选择大步长,从而加速了演化过程而且绝对稳定。实验结果表明,分割速度相对于经典的C-V模型有了很大的提高,而精度损失可以忽略。     

Ranking opportunity sets in the space of functionings

We develop a ranking of compact, convex and comprehensive opportunity sets defined in the evaluative space of individual functionings. We suppose the existence of a target, that is a multi-dimensional bliss point in terms of functionings. This leads us to define concepts such as essentiality and freedomin a novel way. As a main result, we give an axiomatic characterization of the ranking obtained by minimizing the Euclidean distance between each opportunity set and the target.     

数据挖掘方法的应用——对移动通讯用户消费水平的分析

本文将数据挖掘中事务数据库压缩的方法应用于对某县的移动通讯用户从2001年的9月到2002年的3月的消费水平的分析,并对消费情况进行了进一步的剖析,取得了良好的效果。     

Kakutani不动点定理的泛函分析证法

本文讨论了Ｋａｋｕｔａｎｉ不动点定理的泛函分析证法，给出了它的一个较简洁的证明。     

Asymptotic Properties for a Class of Partially Identified Models

We propose inference procedures for partially identified population features for which the population identification region can be written as a transformation of the Aumann expectation of a properly defined set valued random variable (SVRV). An SVRV is a mapping that associates a set (rather than a real number) with each element of the sample space. Examples of population features in this class include interval‐identified scalar parameters, best linear predictors with interval outcome data, and parameters of semiparametric binary models with interval regressor data. We extend the analogy principle to SVRVs and show that the sample analog estimator of the population identification region is given by a transformation of a Minkowski average of SVRVs. Using the results of the mathematics literature on SVRVs, we show that this estimator converges in probability to the population identification region with respect to the Hausdorff distance. We then show that the Hausdorff distance and the directed Hausdorff distance between the population identification region and the estimator, when properly normalized by , converge in distribution to functions of a Gaussian process whose covariance kernel depends on parameters of the population identification region. We provide consistent bootstrap procedures to approximate these limiting distributions. Using similar arguments as those applied for vector valued random variables, we develop a methodology to test assumptions about the true identification region and its subsets. We show that these results can be used to construct a confidence collection and a directed confidence collection. Those are (respectively) collection of sets that, when specified as a null hypothesis for the true value (a subset of values) of the population identification region, cannot be rejected by our tests.     

在数据库中挖掘定量关联规则的方法研究

数据挖掘技术是实现智能决策支持系统的一个重要手段 ,关联规则是数据挖掘的一个重要内容 .传统的 Apriori算法仅适用于挖掘数据间的定性关联关系 ,但数据间的定量关联关系对决策更有帮助 .属性值的离散映射是挖掘定量关联规则的一个重要环节 ,离散映射中属性值区间的划分粒度是影响数据挖掘质量的一个重要因素 .本文结合粗集理论提出了一个确定属性值划分粒度的方法 ,在此基础上设计出一个挖掘定量关联规则的算法 :Apriori 2 ,利用Apriori 2可以挖掘出大量对决策有帮助的定量关联规则     

Quality Control Chart for the Mean using Double Ranked Set Sampling

In this paper, an attempt is made to develop Quality Control Charts for monitoring the process mean based on Double Ranked Set Sampling (DRSS) rather than the traditional Simple Random Sampling (SRS). Considering a normal population and several shift values, the performance of the Average Run Length (ARL) of these new charts was compared with the control charts based on Ranked Set Sampling (RSS) and SRS with the same number of observations. It is shown that the new charts do a better job of detecting changes in process mean compared with SRS and RSS.