基于方案综合评价值和效用值偏差的多属性决策方法

研究了属性权重未知、方案偏好为效用值的多属性决策问题,提出了一种多属性决策模型.首先,分析了部分文献中利用方案属性值与效用值的偏差建模求出属性权重的不合理性,以及根据方案综合评价值与效用值的偏差建模得到属性权重的合理性.然后,通过最小化方案综合评价值与效用值的偏差,建立了一个规划模型,计算出属性权重,进而利用方案综合评价值实现方案的排序择优.最后,通过一个实例说明了方法的可行性.     

一种有方案偏好的直觉模糊多属性决策方法

研究了方案属性值和偏好值均为直觉模糊数的多属性决策问题.首先,通过分析部分文献中利用方案属性值与偏好值之间的偏差建立并求解规划模型,从而得到属性权重的不合理性.其次,在最小化方案综合评价值与偏好值偏差的基础上,建立并求解一个规划模型计算出属性权重.然后,利用方案综合评价值的得分函数和准确度函数对方案进行排序,从而得到了一种有方案偏好的直觉模糊数多属性决策方法.最后,通过一个实例说明了该方法的合理性与有效性.     

另一种新的多指标综合评判方法

指出由决策方案在理想方案上的投影及其到理想方案的距离还不能完全确定决策方案的优劣,应该综合考虑决策方案与理想方案的相似系数及其到理想方案的距离.构造了一种基于相似系数和距离的贴近度,给出了一种新的多指标综合评价方法.方法的有效性和合理性通过实例得到了验证.     

两个无证书签名方案的分析与改进

无证书密码体制结合了基于身份密码体制和传统公钥密码体制的优点,受到了密码和信息安全研究者的极大关注.对梁景玲等提出的消息可恢复的无证书签名方案和侯红霞等提出的无证书短签名方案进行安全性分析,发现两个方案都不能抵抗公钥替换攻击.通过修改签名者密钥生成算法,增加对用户公钥的有效性验证,提高了梁等方案的安全性.通过在签名阶段将用户公钥绑定进HaSh函数,弥补了侯等方案的安全缺陷.     

M/E_k/1排队系统的最优流控制

通过两种经典方案,即非合作最优化方案和合作最优化方案,对M/E_k/1排队系统的最优流控制问题进行研究.在这两种方案下,给出了最优流控制的解,并对解的性质进行分析.     

基于模糊熵的直觉模糊多属性群决策方法

针对专家权重未知、专家判断信息以直觉模糊集给出的多属性群决策问题,提出了一种新的决策方法.通过定义直觉模糊集的模糊熵计算专家判断信息的模糊程度,进而确定每位专家的权重.然后定义直觉模糊集的模糊交叉熵确定备选方案距理想方案和负理想方案的距离,再根据加权算术算子集结专家的判断信息,得到方案的排序.最后,通过一个实例分析验证了方法的有效性.     

一种改进的代理多重签名方案

周等人提出的一种代理多重签名方案由于执行效率高、实现相对简单,因而有着广泛的应用.通过对该方案进行安全性分析,指出该方案容易受到内外两种伪造攻击,因此在安全性上有所欠缺,同时文中给出了相应的攻击方法.最后提出一种新的改进方案,通过加入公钥验证和签名参数处理机制,从而能够有效抵抗内外两种伪造攻击.     

一个数字签名方案中签名者ID的选择与实现

分析了HLL方案的错误,证明了通过适当选择代理签名者Pi的身份参数(ID)能够弥补HLL方案的缺陷,并且不会增加签名过程的计算复杂性.使得改进后的HLL方案是一个安全、实用的门限代理签名方案.     

基于Lagrange插值多项式的接近性关联度决策模型及其应用

针对实数情形的灰色关联决策问题,提出了一种选择最优方案的新方法:首先计算出各个方案和理想方案的Lagrange插值多项式,并利用黎曼积分计算出各个方案和理想方案两曲线之间的面积,进而构造接近性关联度,通过比较接近性关联度来选择最优方案.然后针对区间灰数情形,提出分别计算出各方案的下界序列到理想方案的下界序列的接近性关联度,各方案的上界序列到理想方案的上界序列的接近性关联度后再计算两者的新型综合关联度,并通过比较综合关联度来选择最优方案.最后通过实数和区间灰数两个不同的算例来进一步验证了该方法的可行性、合理性和有效性.     

基于前景理论的多目标灰色关联决策方法

针对决策者风险态度对多目标决策的影响,提出了一种基于前景理论的多目标灰色关联决策方法.通过奖优罚劣的线性变换算子对原始数据进行规范化处理,得到不同目标下的正负理想方案.以各方案与正负理想方案的关联系数为参照点计算各方案的前景矩阵,在此基础上构建方案综合前景值最大化的优化模型,求出不同目标下的最优权向量,进而求出综合效果测度矩阵,对每个局势进行排序.最后通过实例验证了该方法的有效性和实用性.     

离散纵标方法诸格式的误差分析

In this paper some common used numerical schemes for solving discrete ordinate equations are considered and the error estimates are studied for the combined spatial and angular approximations.The conclusions show that the error order of scalar flux in all of these schemes can not be second order even if the source term f is smooth enouth.In addition,when we introduce a kind of graded grids,the simple step character scheme has same accuracy as “high order“ ones.     

Estimation of Binary Markov Random Fields Using Markov chain Monte Carlo

This article compares three binary Markov random fields (MRFs) which are popular Bayesian priors for spatial smoothing. These are the Ising prior and two priors based on latent Gaussian MRFs. Concern is given to the selection of a suitable Markov chain Monte Carlo (MCMC) sampling scheme for each prior. The properties of the three priors and sampling schemes are investigated in the context of three empirical examples. The first is a simulated dataset, the second involves a confocal fluorescence microscopy dataset, while the third is based on the analysis of functional magnetic resonance imaging (fMRI) data. In the case of the Ising prior, single site and multi-site Swendsen-Wang sampling schemes are both considered. The single site scheme is shown to work consistently well, while it is shown that the Swendsen-Wang algorithm can have convergence problems. The sampling schemes for the priors are extended to generate the smoothing parameters, so that estimation becomes fully automatic. Although this works well, it is found that for highly contiguous images fixing smoothing parameters to very high values can improve results by injecting additional prior information concerning the level of contiguity in the image. The relative properties of the three binary MRFs are investigated, and it is shown how the Ising prior in particular defines sharp edges and encourages clustering. In addition, one of the latent Gaussian MRF priors is shown to be unable to distinguish between higher levels of smoothing. In the context of the fMRI example we also undertake a simulation study.     

Optimizing the rank position of the DMU as secondary goal in DEA cross-evaluation

Cross-efficiency evaluation is an extension of Data Envelopment Analysis (DEA) that permits not only the determination of a ranking of Decision Making Units (DMUs) but also the elimination of unrealistic weighting schemes, thereby rescinding the necessity for the inclusion of individual judgements in the models. The main deficiency of the procedure is the non-uniqueness of the optimal weights, which results in the peer evaluations dependences, for instance, on the software used to determine DMU’s efficiencies. This shortfall justifies the inclusion of secondary goals in order to determine cross-efficiency values. In this paper a new proposal of a secondary goal is studied. The idea is related with that proposed in Wu et al. (2009), in which the objective is the optimization of the rank position of the DMU under evaluation. In the procedure proposed here, an incentive to break level-pegging ties between alternatives is introduced by considering that efficiency scores induce a weak order of alternatives. The model is illustrated with a preference-aggregation application.     

A Note on the Stability of the Integral-Differential Equation of the Hyperbolic Type in a Hilbert Space

In this paper, the initial-value problem for integral-differential equation of the hyperbolic type in a Hilbert space H is considered. The unique solvability of this problem is established. The stability estimates for the solution of this problem are obtained. The difference scheme approximately solving this problem is presented. The stability estimates for the solution of this difference scheme are obtained. In applications, the stability estimates for the solutions of the nonlocal boundary problem for one-dimensional integral-differential equation of the hyperbolic type with two dependent limits and of the local boundary problem for multidimensional integral-differential equation of the hyperbolic type with two dependent limits are obtained. The difference schemes for solving these two problems are presented. The stability estimates for the solutions of these difference schemes are obtained.     

带扩散项四阶抛物方程的一类本性并行差分格式

给出逼近带扩散项四阶抛物方程一组非对称差分格式,对此组非对称格式重新组合,得到了一类新的具有并行本性的算法.随后,利用矩阵法证明了算法的绝对稳定性.最后给出数值实验.     

New kinds of solitons and periodic solutions to the generalized KdV equation

In this work, the sine‐cosine method, the tanh method, and specific schemes that involve hyperbolic functions are used to study solitons and periodic solutions governed by the generalized KdV equation. New solutions are determined by using the hyperbolic functions schemes. The study introduces new approaches to handle nonlinear PDEs in the solitary wave theory. © 2006 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq 23: 247–255, 2007     

Nonstandard Finite Difference Schemes for Differential Equations

This paper gives an introduction to nonstandard finite difference methods useful for the construction of discrete models of differential equations when numerical solutions are required. While the general rules for such schemes are not precisely known at the present time, several important criterion have been found. We provide an explanation of their significance and apply them to several model ordinary and partial differential equations. The paper ends with a discussion of several outstanding problems in this area and other related issues.     

A rational approximation and its applications to nonlinear partial differential equations on the whole line

An orthogonal system of rational functions is discussed. Some inverse inequalities, imbedding inequalities and approximation results are obtained. Two model problems are considered. The stabilities and convergences of proposed rational spectral schemes and rational pseudospectral schemes are proved. The techniques used in this paper are also applicable to other problems on the whole line. Numerical results show the efficiency of this approach.     

Analysis and application of finite volume element methods to a class of partial differential equations

The finite volume element (FVE) methods for a class of partial differential equations are discussed and analyzed in this paper. The new initial values are introduced in the finite volume element schemes, and we obtain optimal error estimates in Lp and W1,p (2?p?∞) as well as some superconvergence estimates in W1,p (2?p?∞). The main results in this paper perfect the theory of the finite volume element methods.     

Generalized particle swarm optimization algorithm - Theoretical and empirical analysis with application in fault detection

A generalization of the particle swarm optimization (PSO) algorithm is presented in this paper. The novel optimizer, the Generalized PSO (GPSO), is inspired by linear control theory. It enables direct control over the key aspects of particle dynamics during the optimization process. A detailed theoretical and empirical analysis is presented, and parameter-tuning schemes are proposed. GPSO is compared to the classical PSO and genetic algorithm (GA) on a set of benchmark problems. The results clearly demonstrate the effectiveness of the proposed algorithm. Finally, an application of the GPSO algorithm to the fine-tuning of the support vector machines classifier for electrical machines fault detection is presented.