基于Kohonen网络的激光超声缺陷探测研究

在激光超声缺陷检测技术中,不同类型缺陷采样信号的准确分类至关重要.针对激光超声表面波实验采样信号高维小样本的特点,采用了一种有监督学习的Kohonen神经网络(S_Kohonen)自适应分类方法.在S_Kohonen网络自组织学习的过程中,通过改进网络的学习率提高了网络的收敛速度.通过采用一种无需邻域半径判断的自适应权值调整方式来实现竞争层神经元权值不同程度的调整,从而更有效的表征输入样本的分布特征.通过对不同类型缺陷探测样本的多次实验,验证了所述方法具有良好的分类预测效果,多次交叉验证分类正确率均能达到100%.     

一种群决策专家客观权重确定的改进方法

针对传统的群决策专家客观权重确定的两类主要方法(即基于判断矩阵一致性程度和基于专家排序向量或判断矩阵元素的聚类分析)的缺陷,提出了一种综合考虑两方面因素的改进方法.首先利用聚类分析方法根据各专家的排序向量得到专家类别间的权重,然后根据单个专家的判断矩阵一致性以及排序向量到类核心的距离确定最终权重.文末给出的算例表明该方法是可行、有效的.     

一类无穷维Hamilton算子根向量组的完备性

本文研究主对角元为常数的无穷维Hamilton算子的特征值问题.基于次对角元乘积的特征值和特征向量的某些性质,刻画此类Hamilton算子特征值分布、特征值的代数指标、特征向量(或一阶根向量)的辛正交关系及特征向量组和根向量组在辛Hilbert空间中完备的充要条件.     

三角模糊数判断矩阵理论及排序方法研究

由于三角模糊数运算的复杂性和特殊性,许多经典判断矩阵的理论并非完全适用于三角模糊数判断矩阵.首先指出目前文献中三角模糊数判断矩阵排序向量研究中存在的问题,并对经典判断矩阵的理论和性质在三角模糊数中是否完全适用进行了证明,然后基于已经证明在三角模糊数判断矩阵所适用的性质,分别建立了最小二乘法的三角模糊数互反互补判断矩阵目标优化模型,通过求解其模型可得到矩阵的权重向量,最后利用已有的三角模糊数排序公式求得决策结果.算例分析验证了该方法的正确性和有效性.     

集中阻尼弦本征解的性质

利用Dirac δ函数,在全域建立并求解集中阻尼弦的动力学方程,导出其本征方程组、频率方程和本征函数的一般形式,推导了单项阻尼下本征函数的具体形式,并分析了中点阻尼对本征解的影响.同时,讨论了混合动力学系统在频率 阻尼关系、衰减率和完全抑制振动的最优阻尼3个方面既不同于连续系统,又不同于离散系统的特性:1）系统频率与其阻尼无关;2）各阶本征函数在单位时间内的衰减率都相同,衰减率与本征值的阶次无关;3）当阻尼取2时,系统衰减率趋于无穷大,系统不能发生任何有阻尼振动.     

基于客体优势识别的自主式竞优评析方法

针对多指标描述的一类社会事物群体,提出了一种基于客体优势识别视角的自主式竞优评析方法。该方法以最大程度的尊重事物客体的内质差异为出发点,将竞优评析过程分为个体优势识别、竞优模式识别和模式话语权重确定三个阶段。首先在不违背群体公共价值观念的前提下,从最有利于事物个体的角度识别其优势特征;其次利用聚类分析的方法,从优势特征集中识别群体竞优行为的模式特征;然后以各竞优模式包含的个体数量和评价信息的差异程度为基准确定各模式的话语权重,并得到最终的评价结果。最后以一个算例证明了方法的可行性和有效性。     

群决策中判断的一致性分析及方案排序

对群决策中基于区间数互补判断矩阵形式偏好信息的一致性问题进行了探讨。首先,给出了区间数互补判断矩阵相容性指标定义及性质;然后,通过定义群和个体判断的一致性指标,提出了群判断的一致性判别方法和不一致的协调方法;同时依据个体判断的一致性指标大小,即群成员评判水平的高低,给出了群偏好信息的集结方法;最后给出了由区间数互补判断矩阵导出方案排序权重的计算方法及应用算例。     

不同直觉偏好信息下的多属性决策方法

研究了属性值为实数且决策者对属性的偏好信息以直觉判断矩阵或残缺直觉判断矩阵给出的模糊多属性决策问题.首先介绍了直觉判断矩阵、一致性直觉判断矩阵、残缺直觉判断矩阵、一致性残缺直觉判断矩阵等概念,而后分别考虑关于直觉判断矩阵和残缺直觉判断矩阵的多属性决策问题,接着建立了基于直觉判断矩阵和残缺直觉判断矩阵的多属性群决策模型,通过求解这些模型获得属性的权重.进而给出了不同直觉偏好信息下的多属性决策方法.最后通过一个例子说明了该方法的可行性和实用性.     

弹性理论中上三角无穷维Hamilton算子根向量组的完备性

考虑弹性力学中一类上三角无穷维 Hamilton 算子．首先,给出此类Hamilton算子特征值的几何重数和代数指标,进而得到代数重数．其次,根据Hamilton算子特征值的代数重数确定其特征(根)向量组完备的形式,得到此类Hamilton算子特征(根)向量组的完备性是由内部算子特征向量组决定．最后,将所得结果应用到弹性力学问题中．     

Avoiding infeasibility in DEA models with weight restrictions

The flexibility of weights assigned to inputs and outputs is a key aspect of DEA modeling. However, excessive weight variability and implausible weight values have led to the development of DEA models that incorporate weight restrictions, reflecting expert judgment. This in turn has created problems of infeasibility of the corresponding linear programs. We provide an existence theorem that establishes feasibility conditions for DEA multiplier programs with weight restrictions. We then propose a linear model that tests for feasibility and a nonlinear model that provides minimally acceptable adjustments to the original restrictions that render the program feasible. The analysis can be applied to restrictions on weight ratios, or to restrictions on virtual inputs or outputs.     

不确定型模糊判断矩阵一致性逼近与权重计算的一种方法

西方根据模糊判断矩阵一致性的定义，提出了区间数模糊判断矩阵一致性逼近与排序的方法：该方法充分利用区间数模糊判断矩阵信息构造一致性模糊判断矩阵进行一致性逼近，基于误差传递理论计算排序权重区间，最后给出了一个算例。     

Completeness of the System of Root Vectors of 2 × 2 Upper Triangular Infinite-Dimensional Hamiltonian Operators in Symplectic Spaces and Applications

The authors investigate the completeness of the system of eigen or root vectors of the 2 × 2 upper triangular infinite-dimensional Hamiltonian operator H ₀. First, the geometrical multiplicity and the algebraic index of the eigenvalue of H ₀ are considered. Next, some necessary and sufficient conditions for the completeness of the system of eigen or root vectors of H ₀ are obtained. Finally, the obtained results are tested in several examples.     

Combining Independent Information in a Multivariate Calibration Problem

The problem of combining independent information from different sources in a multivariate calibration setup is considered. The dimensions of the response vectors from various sources may be unequal. A linear combination of the classical estimators based on the individual sources is proposed as an estimator for the unknown explanatory variable. It is shown that the combined estimator has finite mean provided the sum of the dimensions of the response vectors exceeds one and has finite mean squared error if it exceeds two. Expressions for asymptotic bias and mean squared error are given.     

A mathematical programming model for the fuzzy analytic network process—applications of international investment

Currently, the analytic network process (ANP) method is widely employed to consider the multiple criteria analysis problems with dependence and feedback effects. However, in order to extend the ANP to resolve the problem of uncertainty or human subjective judgment, the concepts of fuzzy numbers should be incorporated into the ANP to represent the subjective uncertain pairwise judgments. In this paper, therefore, we propose a novel fuzzy analytic network process (FANP) model by solving a mathematical programming problem. Unlike other FANPs, the proposed method does not require the reciprocity assumption of the weight ratios between criteria, and it can derive local and global weights simultaneously in a single model. Two numerical examples of international investment problems are used to demonstrate the proposed method.     

基于主客体协作的群组评价方法

针对多属性综合评价问题,提出了一种主客体协作式群组评价方法。首先介绍了评价客体意见的获取手段,给出了一种在评价客体没有能力提供自身意见的情况下的意见提取方法;然后以评价主体提供的信息检验评价客体提供信息的失真程度,以评价客体提供的信息检验评价主体提供信息并判定评价主体的能力大小,据此给出评价客体和评价主体的话语权重;根据"差异驱动"原理,集成评价客体群体给出的评价结果和评价主体群体给出的评价结果,得到最终的评价结论。最后,用一个算例检验了方法的有效性。     

模糊判断矩阵排序向量的确定方法研究

首先给出模糊判断矩阵的两种一致性的定义。然后分析现有确定模糊判断矩阵排序向量的方法的特点及存在的问题，在此基础上，系统研究确定模糊判断矩阵排序向量的两类方法，第一类方法是先将模糊判断矩阵转化为AHP判断矩阵，然后将后者的排序向量作为前者的排序向量；另一类方法是直接由一致性或具有满意一致性的模糊判断矩阵计算排序向量。最后用算例说明所提出方法的应用。     

Dimension estimation in sufficient dimension reduction: A unifying approach

Sufficient Dimension Reduction (SDR) in regression comprises the estimation of the dimension of the smallest (central) dimension reduction subspace and its basis elements. For SDR methods based on a kernel matrix, such as SIR and SAVE, the dimension estimation is equivalent to the estimation of the rank of a random matrix which is the sample based estimate of the kernel. A test for the rank of a random matrix amounts to testing how many of its eigen or singular values are equal to zero. We propose two tests based on the smallest eigen or singular values of the estimated matrix: an asymptotic weighted chi-square test and a Wald-type asymptotic chi-square test. We also provide an asymptotic chi-square test for assessing whether elements of the left singular vectors of the random matrix are zero. These methods together constitute a unified approach for all SDR methods based on a kernel matrix that covers estimation of the central subspace and its dimension, as well as assessment of variable contribution to the lower-dimensional predictor projections with variable selection, a special case. A small power simulation study shows that the proposed and existing tests, specific to each SDR method, perform similarly with respect to power and achievement of the nominal level. Also, the importance of the choice of the number of slices as a tuning parameter is further exhibited.     

Eigenweight vectors and least-distance approximation for revealed preference in pairwise weight ratios

A new eigenweight vector is derived for the data of pairwise weight ratios. The well-known eigenweight vector derived by Saaty is then compared and contrasted in the light of least-distance approximation models. It is shown that the new eigenweight vector commands advantages over Saaty's, including less rigid assumptions on the error terms, robustness of solution, in addition to the fact that the new eigenweight vector can be computed very easily. The reader can construct other types of eigenweight vectors and least-distance approximation models using the framework of this article.Dedicated to G. Leitmann     

Normalization of sunspot cycles and eigen mode analysis

The smoothed monthly sunspot numbers of the previous 22 complete sunspot cycles are normalized in time domain, and then an eigen mode analysis is carried out to draw the principle factors (or components) in the cycles. The results show that the main characteristics of the solar cycles can be described fairly well by the first 5 eigen modes. The obtained eigen modes are used to predict the declining phase of cycle 23 on the basis of the data prior to its maximum. The prediction indicates that cycle 23 will last for 127 months to December 2006, with the minimum of 6.2.     

加权幂平均复合判断矩阵的群体AHP一致性研究

判断矩阵一致性是群体综合评价的重要内容。一致性可以反映专家群体就所有可能的替代方案达成完全一致的意见,利用一致性测度可以衡量评价者之间的差异,也是共识判断的基础。文章针对加权幂平均复合判断矩阵中存在的一致性问题进行研究,从幂平均次数对复合判断矩阵影响的角度,分析幂平均次数对一致性比率的影响程度,揭示两者之间的变化规律;研究专家判断矩阵的阶数对一致性比率的作用;从群体专家权重的角度,研究权重对复合判断矩阵一致性比率的敏感性。结果表明:(1)幂平均的次数会影响复合判断矩阵的一致性,幂平均的次数在有限区间内保证复合判断矩阵的满意一致性,有限区间长度同时依赖于判断矩阵的阶数与幂平均的次数,且一致性比率是关于幂平均次数的凹函数;(2)判断矩阵的阶数越高,专家给出判断矩阵的难度加大,然而随着判断矩阵阶数的升高,一致性比率会呈递减趋势;(3)专家的权数对于一致性比率影响较小,即专家权数的灵敏度较低,给定权数的扰动后不会影响复合判断矩阵的满意一致性。