条件(Q)条件(PT)对推出图及Rees商的传递性

研究了条件(Q)、条件(PT)对推出图、Rees商的传递性,证明了在推出图(1)中P满足条件(Q)当且仅当Y满足条件(Q)并且f是酉的.     

基于遗传算法和模拟退火的插值马氏模型用于真核生物启动子识别

启动子识别是生物信息学领域极具挑战的问题.本文在IMC(Interpolated Markov Chin)的框架下考虑碱基的插入与缺失,采用SA(Simulated Annealing)训练转移概率,以增加模型的鲁棒性,利用GA(Genetic Algorithm)优化IMC插值系数,以克服梯度算法易于陷入局部极值点的缺陷,最后将该模型用于启动子识别,识别率在测试集达到86%.     

T-染色和G_n~d图的T-边柞(Edge Span)

给定一个包含0的有限正整数集T,一个简单图G的一个T-染色是定义在G的顶点集V(G)上的一个非负函数f,满足对任意的uv∈E(G)有|f(u)-f(v)| T.一个T-染色f的边柞(edgespan)定义为最大的|f(x)-f(y)|,xy∈E(G),一个图G的边柞(edgespan)是G的所有T-染色中最小的边柞(edgespan).这篇文章研究了当T={0,1,2,…,k-1}时,Gdn图的T-边柞(edgespan),找到了当n≡1(modd)时Gdn图的T-边柞(edgespan)的确切值,和其他情况下的上下界.     

关于Smarandache函数S(n)与除数函数d(n)的混合均值

对于任意的正整数n,著名的Smarandache函数S(n)定义为最小的正整数m,使得n|m!,即就是S(n)=min{m:n|m!,m ∈N).本文的主要目的是应用初等方法研究S(n)与除数函数d(n)的加权均值问题,并获得一个有趣的渐进公式.     

参数d迭代逼近的GM(1,1)模型及其在技术创新中的应用

首先论述了参数d迭代逼近求解的GM(1,1)模型基本思路.其次,给出了此模型的参数估计与算法,即:1)估算出初始a_l,根据GM(1,1)模型a,c,d之间的关系,由a_l求得C_l,d_l;2)迭代d_l→d_(l+1),再计算a_(l+1),c_(l+1)及平均相对误差mape_l,mape_(l+1);3)多次迭代d_l→d_(l+1),直至|mape_(l+1)-mape_l|ε时,可得mape最小时的最优参数a,c,d值.然后,从理论与实证方面,证明模型是无偏的,且在参数d迭代过程中,a总能取到有意义的值.最后将模型应用于企业技术创新领域之中.     

T-染色和Gdn图的T-边柞(Edge Span)

给定一个包含0的有限正整数集T,一个简单图G的一个T-染色是定义在G的顶点集V(G)上的一个非负函数f,满足对任意的uv∈E(G)有|f(u)-f(v)| T.一个T-染色f的边柞(edge span)定义为最大的|f(x)-f(y)|,xy∈E(G),一个图G的边柞(edge span)是G的所有T-染色中最小的边柞(edge span).这篇文章研究了当T={0,1,2,…,k-1}时,Gdn图的T-边柞(edge span),找到了当n≡1(mod d)时Gdn图的T-边柞(edge span)的确切值,和其他情况下的上下界.     

不定最小二乘问题的改进的不完全双曲Gram-Schmidt预处理算法

应用改进的不完全双曲Gram-Schmidt(IHMGS)方法预处理不定最小二乘问题的共轭梯度法(CGILS)、正交分解法(ILSQR)与广义的最小剩余法(GMRES)等迭代算法来求解大型稀疏的不定最小二乘问题.数值实验表明,IHMGS预处理方法可有效提高相应算法的迭代速度,且当矩阵的条件数比较大时,效果更加显著.     

基于卡尔曼滤波的MGM-多维AR(p)模型的构建及其应用

由于受到外界不确定性因素的干扰,导致实际数据偏离模拟的趋势,使得灰色多变量MGM(1,m)模型预测效果不佳,而多维平稳序列自回归模型(AR(p))能够有效反应具体数据与整体趋势之间产生的偏差,从而可以掌握外界环境对目标数据发展趋势带来的影响.由此文章首先利用卡尔曼滤波对给定的小样本数据做平滑处理,消除数据观测时产生的噪...     

SV-M模型下VaR和ES估计的极值方法

本文对随机波动均值内模型(SV-M)应用极值理论(EVT)的方法估计了金融回报的风险价值(VaR)和期望短缺(ES).用SV-M建模异方差金融回报时间序列,刻画了其波动聚类.用蒙特卡罗极大似然方法(MCL)来估计其参数.我们用基于一般帕累托分布(GPD)的EVT拟合SV-M模型的修正分布尾部,刻画了金融时序分布的肥尾特性.因此,本文的极值方法有效地克服了原有方法的缺陷,综合考虑了金融时序的波动聚类及其分布的肥尾特性,给出了合理的VaR和ES估计,对市场风险测度的研究进行了有益的探讨.     

群中的一个组合问题

设 G 是一个有限 Abelian 群,c(G)是最小正整数使得 G 的任一合于|S|(?)c(G),0(?)S 的子集 S 均有性质:G 的每一个元均可表为 S 的某些元之和.本文确定了一大类 Abelian 群 G 相应的 c(G).对大部分有限交换 P-群 G,c(G)已由本文确定.     

System identification of a pick-and-place mechanism driven by a permanent magnet synchronous motor

The main objective of this study is to utilize two dynamic models: a mathematical model and a simple model, to identify a pick-and-place mechanism (PPM) which is driven by a permanent magnet synchronous motor (PMSM). In this paper, Hamilton’s principle is employed to derive the mathematical model, which is a nonlinear differential equation, while Newton’s second law is utilized to derive the simple linear model. In system identification, we adopt the real-coded genetic algorithm (RGA) to find not only the parameters of the PPM, but also the PMSM simultaneously. From the identification simulations and experimental results, it is demonstrated that the identification results of the mathematical model present the better matching with the experimental results of the system.     

模糊数学在企业创新能力评定中的应用

建立了企业创新的系统结构图,并利用模糊综合评价法建立了一个对企业创新能力进行评定的三级模糊综合评判的数学模型,采用层次分析法更加合理的确定了权重的分配,通过实例说明了该模型的使用方法,得到了更有效的结果.     

基于产品合格率随机分布的投产量模型优化

当产品合格率随机分布时,建立起以投产综合损失费(欠产再投产费,过量产出费和不合格处理费)期望值为目标函数,投产量为决策变量的投产量模型。结合产品合格率随机分布不同(均匀分布和正态分布)推导求解了模型的最优解,得出了产量与投产量的等式关系,据此可有效地指导生产。最后通过数值实例分析验证了模型的有效性。     

A multi-objective joint replenishment inventory model of deteriorated items in a fuzzy environment

In this study, a fuzzy multi-objective joint replenishment inventory model of deteriorating items is developed. The model maximizes the profit and return on inventory investment (ROII) under fuzzy demand and shortage cost constraint. We propose a novel inverse weight fuzzy non-linear programming (IWFNLP) to formulate the fuzzy model. A soft computing, differential evolution (DE) with/without migration operation, is proposed to solve the problem. The performances of the proposed fuzzy method and the conventional fuzzy additive goal programming (FAGP) are compared. We show that the solution derived from the IWFNLP method satisfies the decision maker’s desirable achievement level of the profit objective, ROII objective and shortage cost constraint goal under the desirable possible level of fuzzy demand. It is an effective decision tool since it can really reflect the relative importance of each fuzzy component.     

Optimal reinsurance under dynamic VaR constraint

This paper deals with the optimal reinsurance strategy from an insurer’s point of view. Our objective is to find the optimal policy that maximises the insurer’s survival probability. To meet the requirement of regulators and provide a tool to risk management, we introduce the dynamic version of Value-at-Risk (VaR), Conditional Value-at-Risk (CVaR) and worst-case CVaR (wcCVaR) constraints in diffusion model and the risk measure limit is proportional to company’s surplus in hand. In the dynamic setting, a CVaR/wcCVaR constraint is equivalent to a VaR constraint under a higher confidence level. Applying dynamic programming technique, we obtain closed form expressions of the optimal reinsurance strategies and corresponding survival probabilities under both proportional and excess-of-loss reinsurance. Several numerical examples are provided to illustrate the impact caused by dynamic VaR/CVaR/wcCVaR limit in both types of reinsurance policy.     

Minimizing loss probability bounds for portfolio selection

In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional programs are derived for constructing portfolios, where the numerator of the ratio in the objective includes the value-at-risk (VaR) or conditional value-at-risk (CVaR) while the denominator is any norm of portfolio vector. Depending on the parameter values in the model, the derived formulations can result in a nonconvex constrained optimization, and an algorithm for dealing with such a case is proposed. Some computational experiments are conducted on real stock market data, demonstrating that the CVaR-based fractional programming model outperforms the empirical probability minimization.     

基于多属性群决策的软件可靠性模型选择方案

基于模型评价准则的属性值和专家经验的偏好信息进行软件可靠性增长模型的选择,可归结为多属性群决策问题.模型评价准则的主观专家偏好可利用层次分析方法将其量化得到其主观排序权重;基于失效数据的模型评价准则的计算数据可利用熵权法获得客观排序权重.综合两种权重可得到既能反映专家经验又能综合实际软件可靠性测试数据的模型评价方案.实例分析表明该方案具有合理性和可行性.     

Numerical simulation of pollutant dispersion in street canyons: Geometric and thermal effects

Numerical investigations on pollutant dispersion in street canyons with emission sources located near the ground level are performed in the present work. Pollutant dispersion problems in urban areas are usually studied considering the street canyon model, which consists of long streets laterally confined by buildings. Significant changes can be observed in wind flow patterns and pollutant concentration fields when thermal and geometric effects are considered. Thus, the objective of this study is to investigate numerically the wind flow and pollutant dispersion for the following cases: (a) a two-dimensional street canyon model considering three different aspect ratios and four different wall heating configurations; (b) a flow domain with two immersed buildings arranged in two distinct configurations; (c) a three-dimensional urban area model composed of a building set and street intersections. Expected flow structures were obtained inside the canyon when different aspect ratios and wall heating configurations were considered. Flow phenomena such as separation/reattachment were observed when two-buildings models were analyzed. Finally, three-dimensional flow structures, with some characteristic that are not observed in two-dimensional models, affecting the pollutant removal, were simulated in the last case, highlighting the relevance of model dimensionality. The wind flow and pollutant dispersion are investigated using a numerical model based on the finite element formulation utilized by some of the authors of this work, which is extended here to deal with problems of heat and mass transport in the urban micro-scale. Turbulence is reproduced using Large Eddy Simulation (LES) and thermal effects on the momentum equations are considered as a buoyancy force, according to Boussinesq approximation.     

基于SMAA和公共权重的决策单元效率评价与排序

在数据包络分析(DEA)中,公共权重模型是决策单元效率评价与排序的常用方法之一。与传统DEA模型相比,公共权重模型用一组公共的投入产出权重评价所有决策单元,评价结果往往更具有区分度且更为客观。本文考虑决策单元对排序位置的满意程度,提出了基于最大化最小满意度和最大化平均满意度两类新的公共权重模型。首先,基于随机多准则可接受度分析(SMAA)方法,计算出每个决策单元处于各个排名位置的可接受度;然后,通过逆权重空间分析,分别求得使最小满意度和平均满意度最大化的一组公共权重;最后,利用所求的公共权重,计算各决策单元的效率值及相应的排序。算例分析验证了本文提出的基于SMAA的公共权重模型用于决策单元效率评价与排序的可行性。     

Self-adaptive support vector machines: modelling and experiments

Method  In this paper, we introduce a bi-level optimization formulation for the model and feature selection problems of support vector machines (SVMs). A bi-level optimization model is proposed to select the best model, where the standard convex quadratic optimization problem of the SVM training is cast as a subproblem. Feasibility  The optimal objective value of the quadratic problem of SVMs is minimized over a feasible range of the kernel parameters at the master level of the bi-level model. Since the optimal objective value of the subproblem is a continuous function of the kernel parameters, through implicity defined over a certain region, the solution of this bi-level problem always exists. The problem of feature selection can be handled in a similar manner. Experiments and results  Two approaches for solving the bi-level problem of model and feature selection are considered as well. Experimental results show that the bi-level formulation provides a plausible tool for model selection.