陕西省研究生教育预测模型实证研究

在综合考虑陕西省研究生教育的历史和现状、所面临的机遇和挑战,及全省经济发展速度的基础上,运用统计理论中的趋势拟合模型、二次指数平滑模型、线性回归模型,预测陕西省研究生教育在未来10年所应达到的规模.并结合实际对三种预测模型的预测结果进行了讨论,分析了各种预测模型的利与弊,判断出线性回归模型是较为可取的一种模型,从而得出研究生招生规模应量力而行的结论.     

基于混合模型的利润驱动违约判别临界点研究

违约判别临界点是金融机构是否接受客户贷款申请的重要参考,合适的违约判别临界点对减少金融机构贷款损失实现稳健经营具有重要意义。本文研究的问题是如何保证计算客户违约概率的准确性,并找到利润最大化的违约判别临界点。本文的创新与特色:一是通过将多个不同类型的违约判别模型计算的客户违约概率进行加权平均,保证了计算客户违约概率的的整体准确性,避免了使用单一模型计算客户违约概率不准确的弊端;二是通过定义金融机构从贷款中获得利润的计算公式,以利润最大为目标,求解违约判别临界点,避免了现有计算临界点的方法如广义对称点估计和经验似然法等方法得到的临界点利润不是最大的弊端。研究发现:混合模型比单一模型的准确性高,AUC值显著提高;在人人贷数据集中本文的违约判别临界点下贷款利润远高于其他方法下临界点的利润。     

具反馈控制单种群食物有限模型稳定性研究

研究具有反馈控制的食物有限模型．首先探讨了该系统的平衡点的局部稳定性态,其次借助于Bendixson-Dulac判别法证得系统不存在闭轨线,由此知系统的正平衡点是全局吸引的．     

基于主成分和Fisher判别的DNA序列分类方法

统计DNA序列中64种包含3个碱基字符串的频率,基于生物学知识,以此作为区分不同类别DNA序列的特征.对此频率数据使用主成分分析和Fisher判别两种方法进行数据降维操作,根据降维后的数据建立距离判别模型,用训练样本回判,检验模型判别效果,最后对未知类别序列进行判别归类,比较分类结果.     

基于主成分和Fisher判别的DNA序列分类方法北大核心

统计DNA序列中64种包含3个碱基字符串的频率,基于生物学知识,以此作为区分不同类别DNA序列的特征.对此频率数据使用主成分分析和Fisher判别两种方法进行数据降维操作,根据降维后的数据建立距离判别模型,用训练样本回判,检验模型判别效果,最后对未知类别序列进行判别归类,比较分类结果.     

基于贝叶斯逐步判别法的入侵检测模型研究

基于贝叶斯逐步判别法构建入侵检测模型,将入侵检测转化为一个分类判别问题,基于步进式引入的方法淘汰冗余的特征变量,能够在保障判别效果的前提下有效降低原分类判别函数的计算复杂度.使用KDD CUP99数据中10%数据集作为实验数据,以常见的拒绝服务攻击(DoS攻击)为例创建具体的模型实例,实验结果表明,模型对于样本内连接记录的回代判对率和样本外连接记录的检测正确率均较高.     

多标准等级判别模型在信用评级中的应用研究

在对目前我国信用评级方法应用现状分析的基础上,提出改进的多标准等级判别模型.并将该模型应用于商业银行信用风险评估中.通过对银行五级分类贷款样本的实证研究,证实了该判别模型的有效性和先进性.     

带有无限到达源的Re-entrant Line的稳定性

用Foster判别准则,Weiss[13]给出了带有无限到达源的两站三步re-entrant line的稳定性的充分条件．本文用两种不同的方法得到了其稳定性的充分必要条件,即: 二维随机游动方法和Foster判别准则．同时,我们又用流体模型方法得到了系统稳定的充分条件,大大地简化了Weiss[13]的证明．     

明确能力要求 调整试卷结构 实现考核目的——从研究生数学大纲修订看考试改革

当前,研究生招生形势已经发生了很大的变化,随着科技的进步和我国社会经济迅猛发展,全国研究生招生规模大幅度增加,报考人数连年递增,而且还有继续增长的趋势,这使考试的组织和管理的任务加重,难度加大.     

基于形式Context的格聚类与特征逼近判别

本文建立一个基于 Guo- Qiang Zhang[2 ]理论的格聚类模型与特征逼近判别模型 .如果一个统计背景 ET被解释为一个 Context CET=(Po,| =Pa) ,那么基于形式 Context的格聚类模型完全是 [FCA]的外延和内涵统一的具体表达 ,而特征逼近判别模型则是从语义谓词逻辑出发的判别方法 ,用有限特征逼近解决了无限属性的实际应用困难 .     

双准则广义DEA模型及其模糊解法

针对单准则广义DEA模型在评价决策单元有效性时的局限性,从输入和输出两个角度同时考虑,建立了双准则广义DEA模型,并应用模糊数学理论给出其求解方法.     

多准则随机DEA模型及其在投资评价中的应用研究

通过对随机输入、产出指标的分析,在经典ＤＥＡ模型的基础上,增加了多个评价准则,从而提出了多准则随机ＤＥＡ评价模型,最后,通过在项目投资评价中的运用说明了该模型的实用性。     

Q和Q_B准则在Double设计中的应用

一阶回归模型通常用来从众多因子当中筛选出效应显著的因子,而Q和QB准则能够比较简单地从众多合格的拟合子模型中找出最优设计.因此本文主要探讨了极大模型为一阶回归模型时,初始设计d分别与Double设计d dd-d,完全反转设计d-d,设计dd11-dd22之间的关系,以及它们在Q和QB准则下的最优性关系.     

An algorithm for solving a multiple criteria optimal control problem

An algorithm for finding agood solution for a multiple criteria optimal control problem is given. The criteria are assumed to be ordered according to their importance to the decision-maker. The algorithm consists of successive solutions of single criterion optimal control problems. Other criteria are taken into account by adding constraints to the problem in a systematic manner.     

An improvement to multiple criteria ABC inventory classification

In this paper we presented an extended version of the Ng-modelg [W.L. Ng, A simple classifier for multiple criteria ABC analysis, European Journal of Operational Research 177 (2007) 344–353] for multi-criteria inventory classification. The proposed model is a nonlinear programming model which determines a common set of weights for all the items. Our model not only incorporates multiple criteria for ABC classification, but also maintains the effects of weights in the final solution, an improvement over the model proposed by Ng. An illustrative example is presented to compare our model and the Ng-model.     

Multiobjective programming methods in the reserve selection problem

Many ecological criteria have been proposed to assign conservation values to nature reserves in the reserve selection problem. Multiobjective programming is used to identify the best compromise solution among a set of alternative solutions that have been obtained from single objective linear programming methods based upon different criteria. Endemic plant species from the island of Crete in Greece are used as a model and a number of cells, as they have been implemented by ARC/INFO, are selected based on four criteria: (1) species richness, (2) species rarity, (3) cell richness, (4) cell rarity. Best compromise solution is identified by (i) a simple multiattribute rating technique, (ii) geometrical methods based on four distance metrics. The two methods are compared and the degree to which they fulfil the four criteria is examined.     

Robust ordinal regression for value functions handling interacting criteria

We present a new method called UTA^GMS–INT for ranking a finite set of alternatives evaluated on multiple criteria. It belongs to the family of Robust Ordinal Regression (ROR) methods which build a set of preference models compatible with preference information elicited by the Decision Maker (DM). The preference model used by UTA^GMS–INT is a general additive value function augmented by two types of components corresponding to “bonus” or “penalty” values for positively or negatively interacting pairs of criteria, respectively. When calculating value of a particular alternative, a bonus is added to the additive component of the value function if a given pair of criteria is in a positive synergy for performances of this alternative on the two criteria. Similarly, a penalty is subtracted from the additive component of the value function if a given pair of criteria is in a negative synergy for performances of the considered alternative on the two criteria. The preference information elicited by the DM is composed of pairwise comparisons of some reference alternatives, as well as of comparisons of some pairs of reference alternatives with respect to intensity of preference, either comprehensively or on a particular criterion. In UTA^GMS–INT, ROR starts with identification of pairs of interacting criteria for given preference information by solving a mixed-integer linear program. Once the interacting pairs are validated by the DM, ROR continues calculations with the whole set of compatible value functions handling the interacting criteria, to get necessary and possible preference relations in the considered set of alternatives. A single representative value function can be calculated to attribute specific scores to alternatives. It also gives values to bonuses and penalties. UTA^GMS–INT handles quite general interactions among criteria and provides an interesting alternative to the Choquet integral.     

Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach

Selection of supply chain partners is an important decision involving multiple criteria and risk factors. This paper proposes a fuzzy multi-objective programming model to decide on supplier selection taking risk factors into consideration. We model a supply chain consisting of three levels and use simulated historical quantitative and qualitative data. We propose a possibility approach to solve the fuzzy multi-objective programming model. Possibility multi-objective programming models are obtained by applying possibility measures of fuzzy events into fuzzy multi-objective programming models. Results indicate when qualitative criteria are considered in supplier selection, the probability of a certain supplier being selected is affected.     

Global stability of Gompertz model of three competing populations

The model of three competitive populations with Gompertz growth is studied. The periodic solutions are ruled out by generalized Dulac criteria. On the basis of the analysis, we obtain conditions that ensure the asymptotic behavior of the model is simple.     

Stochastic multiobjective acceptability analysis for the Choquet integral preference model and the scale construction problem

The Choquet integral preference model is adopted in Multiple Criteria Decision Aiding (MCDA) to deal with interactions between criteria, while the Stochastic Multiobjective Acceptability Analysis (SMAA) is an MCDA methodology considered to take into account uncertainty or imprecision on the considered data and preference parameters. In this paper, we propose to combine the Choquet integral preference model with the SMAA methodology in order to get robust recommendations taking into account all parameters compatible with the preference information provided by the Decision Maker (DM). In case the criteria are on a common scale, one has to elicit only a set of non-additive weights, technically a capacity, compatible with the DM’s preference information. Instead, if the criteria are on different scales, besides the capacity, one has to elicit also a common scale compatible with the preferences given by the DM. Our approach permits to explore the whole space of capacities and common scales compatible with the DM’s preference information.