基于效用函数的模糊数的比较与排序

对相同的模糊数进行比较,不同风险偏好的决策者,会得到不同的结论.效用函数是对风险偏好的度量,因此,模糊数的比较与排序的方法,一定要结合决策者的效用函数来构造.为此,根据效用函数定义了模糊效用函数,在此基础上定义了效用序.之后,证明效用序为全序,进一步利用结构元理论对效用序进行表述.根据效用函数反映风险偏好的程度,对效用序进行分类.这样,决策者对模糊数进行比较时,依据自身对风险偏好程度来选择效用序.     

Extremal f-trees and embedding spaces for molecular graphs

Problems concerning embedding trees in lattice-graph or Euclidean spaces are considered. A tree is defined to be ‘almost-embeddable’ in a lattice-graph if a sequence derived from the distance degree sequence of the lattice-graph and a corresponding sequence for the tree satisfy a specified inequality. This inequality is such that every tree that is embeddable in the lattice-graph is in the set of almost-embeddable trees. For Euclidean space embeddings the lattice-graph sequence is replaced by a sequence defined in terms of sphere packing numbers. This work has two practical objectives: Firstly, to furnish a framework within which intuitive chemical and physical notions about embedding spaces can be made explicit and self-consistent. Secondly, to obtain useable criteria which will exclude from statistical mechanical averaging procedures those molecular species which are inconsistent with a postulated embedding space. The inequality proposed here meets these objectives for molecular trees and its implications for chemical and physical theory are discussed in some detail.     

Geometry of the vacant set left by random walk on random graphs,Wright's constants,and critical random graphs with prescribed degrees

We provide an explicit algorithm for sampling a uniform simple connected random graph with a given degree sequence. By products of this central result include: (1) continuum scaling limits of uniform simple connected graphs with given degree sequence and asymptotics for the number of simple connected graphs with given degree sequence under some regularity conditions, and (2) scaling limits for the metric space structure of the maximal components in the critical regime of both the configuration model and the uniform simple random graph model with prescribed degree sequence under finite third moment assumption on the degree sequence. As a substantive application we answer a question raised by ?erný and Teixeira study by obtaining the metric space scaling limit of maximal components in the vacant set left by random walks on random regular graphs.     

The distribution of extremes in the degree sequence: A Gumbel distribution approach

In this work, an approximation of the asymptotics of the distribution for the maximum and minimum of the degree sequence defined over a random graph is determined using a new approach in terms of the Gumbel distribution for extremes.     

基于均衡接近度的不确定型的统计决策方法及其应用

灰熵是离散序列均衡程度的测度,灰色关联度是参考序列和比较序列之间接近程度的测度.该方法将灰色关联度和均衡度合成为均衡接近度,并以此作为决策准则,提出了一种不确定型的统计决策的量化计算方法,从而避免了低层次多因素权重确定的主观性,使决策模型更加合理.通过将该决策方法在华油燃气公司决策中进行应用,为该公司选择了最优决策方案,使其获得了较好的经济效益.     

基于区间灰关联度的飞机战术意图识别方法

针对敌机在某一时刻其状态属性特征具有迷惑性的特点将某时间段敌机的特征指标变化序列用区间数表示,然后将敌机各类战术意图基准特征值也用区间数表示,将两类区间数都进行规范化处理,求得目标特征区间值与各类意图基准特征区间值的距离矩阵,用AHP方法确定特征指标权重,然后提出了一个新的灰关联模型并对其满足灰关联四公理进行了证明,用提出的灰关联度模型对目标战术意图进行识别.仿真结果证明区间灰关联度方法用于飞机战术意图识别的有效性,同时可以发现其在实时性方面的优势.     

Fast computation of a rational point of a variety over a finite field

We exhibit a probabilistic algorithm which computes a rational point of an absolutely irreducible variety over a finite field defined by a reduced regular sequence. Its time-space complexity is roughly quadratic in the logarithm of the cardinality of the field and a geometric invariant of the input system. This invariant, called the degree, is bounded by the Bézout number of the system. Our algorithm works for fields of any characteristic, but requires the cardinality of the field to be greater than a quantity which is roughly the fourth power of the degree of the input variety.

     

An Evolving Random Network and Its Asymptotic Structure

In this paper, we propose an evolving random network. The model is a linear combination of preferential attachment model and uniform model. We show that scaling limit distribution of the number of leaves at time $n$ is approximated by normal distribution and the proportional degree sequence obeys power law. The branching structure and maximum degree are also discussed in this paper.     

基于趋势修正的灰预测模型优化研究

针对序列增长趋势不完全满足准指数规律时的灰色预测建模问题,提出基于GM(1,1)模型与序列增长趋势之间偏差修正的建模方法,将GM(1,1)模型还原式中的常数项作为灰变量处理,加入调整系数以缩小拟合值与实际值之间的增长趋势差异,利用灰色离散模型拟合调整系数的变化过程,将得到的调整系数拟合值带入原时间响应函数,进而得到趋势修正的原始序列拟合值;运用新的建模方法对南京市第三产业用电量进行拟合和预测,证明了方法有效提升了GM(1,1)建模精度,并且拟合序列和实际序列的灰色绝对关联度得到提高.     

Ranking and unranking of non-regular trees with a prescribed branching sequence

Ordered trees are called non-regular trees with a prescribed branching sequence (or non-regular trees for short) if their internal nodes have a pre-specified degree sequence in preorder list. This article presents two main results. First, we develop a simple algorithm to generate all non-regular trees in lexicographic order using RD-sequences defined by [R.-Y. Wu, J.-M. Chang, Y.-L. Wang, Loopless generation of non-regular trees with a prescribed branching sequence, The Computer Journal 53 (2010) 661–666]. Then, by analyzing the structure of a coding tree, this algorithm is proved to run in constant amortized time. Next, we propose efficient ranking algorithm (i.e., determining the rank of a given non-regular tree in such an order) and unranking algorithm (i.e., converting a positive integer to its corresponding RD-sequence).     

Random graphs with given vertex degrees and switchings

Random graphs with a given degree sequence are often constructed using the configuration model, which yields a random multigraph. We may adjust this multigraph by a sequence of switchings, eventually yielding a simple graph. We show that, assuming essentially a bounded second moment of the degree distribution, this construction with the simplest types of switchings yields a simple random graph with an almost uniform distribution, in the sense that the total variation distance is o(1). This construction can be used to transfer results on distributional convergence from the configuration model multigraph to the uniform random simple graph with the given vertex degrees. As examples, we give a few applications to asymptotic normality. We show also a weaker result yielding contiguity when the maximum degree is too large for the main theorem to hold.     

Local asymptotic admissibility of a generalization of Akaike's model selection rule

Summary A model selection rule of the form minimize [−2 log (maximized likelihood)+complexity] is considered, which is equivalent to Akaike's minimum AIC rule if the complexity of a model is defined to be twice the number of independently adjusted parameters of the model. Under reasonable assumptions, when applied to a locally asymptotically normal sequence of experiments, the model selection rule is shown to be locally asymptotically admissible with respect to a loss function of the form [inaccuracy+complexity], where the inaccuracy is defined as twice the Kullback-Leibler measure of the discrepancy between the true model and the fitted version of the selected model. This research was supported by NSF Grant No. MCS 80-02732.     

Weak invariance principles for local time

Summary We establish sufficient conditions to ensure that the suitably defined local times of a sequence of random walks converge weakly to Brownian local time.Portions of this material are included in the author's dissertation submitted for the degree of Doctor of Philosophy at the University of Illinois at Urbana-Champaign     

A convergent decomposition method for box-constrained optimization problems

In this work we consider the problem of minimizing a continuously differentiable function over a feasible set defined by box constraints. We present a decomposition method based on the solution of a sequence of subproblems. In particular, we state conditions on the rule for selecting the subproblem variables sufficient to ensure the global convergence of the generated sequence without convexity assumptions. The conditions require to select suitable variables (related to the violation of the optimality conditions) to guarantee theoretical convergence properties, and leave the degree of freedom of selecting any other group of variables to accelerate the convergence.     

一种能够提高建模精度的含参变量函数变换及其应用

根据传统预测模型适宜低增长、近指数序列这一基本事实出发,提出用原始序列本身的级比偏差来比较低增长的程度,用原始序列所对应的级比序列的级比偏差比较原始序列接近指数序列的程度.并通过此种方法找到了一种含参变量函数变换缩小级比偏差的范围,从而提高了建模精度,又因为此种变换能够保证还原相对误差保持不变,以至于最终达到了提高建模精度的目的.并通过实例验证了方法的可行性及其结果的正确性、优越性.     

基于理想灰关联距离的课程评估方法

综合灰色系统理论、理想解法和欧氏距离,提出了一种新的基于理想关联距离度的课程评估方法,给出了建立评估模型的基本步骤.定量处理的指标,经过理想化、标准化后,定义关联数,由此计算关联距离度.通过灰色关联距离度,建立了一种接近最优方案远离最差方案的评估模型.并通过学院近期的课程评估实例分析,验证了该方法的准确性和可行性.     

一类无标度随机图的度序列

本文从-个新的角度对-类随机图的度序列进行了分析.证明了此模型度分布的存在性,得到了网络规模比较大的情况下度为七的节点所占比例数的表达式.此外,我们还将模型扩展到每个时间步增加边数为随机变量的情形,得到了类似的结论.     

Convergence of Utility Indifference Prices to the Superreplication Price: the Whole Real Line Case

A discrete-time financial market model is considered with a sequence of investors whose preferences are described by concave strictly increasing functions defined on the whole real line. Under suitable conditions we prove that, whenever their absolute risk-aversion tends to infinity, the respective utility indifference prices of a given bounded contingent claim converge to the superreplication price. We also prove that there exists an accumulation point of the optimal strategies’ sequence which is a superhedging strategy.     

矩阵型灰色关联度的特征检验方法及其应用

针对现有灰色关联理论的不足, 本文提出灰色关联的特征检验思路, 构建了矩阵型灰色关联度的特征检验方法。首先优化矩阵型灰色关联模型, 利用行为矩阵差值定义特征差异矩阵, 采用矩阵2范数构建关联度公式。而后分析特征差异矩阵的稳定性与趋势性, 利用变异系数形式构建稳定性系数, 利用最小二乘法估计趋势性系数, 两者共同组成矩阵型灰色关联度的特征检验方法。最后, 本文模型被应用于湖北省恩施州的长期多维贫困分析, 在与现有模型的比较中, 发现关联度评估结果有效区分了恩施州8个市县的贫困情况, 特征检验方法从贫困不确定性和趋势性两方面对结果进行补充, 验证了模型的可行性与实用性。