一种基于犹豫模糊集的多属性关联决策方法

主要讨论属性间具有关联性的条件下犹豫模糊多属性决策问题.首先,基于gλ模糊测度,Shapley值和Choquet积分,定义了两种犹豫模糊信息集成算子:AHFGSCgλ算子和GHFGSCgλ算子.然后,讨论了这些算子的一些性质.最后通过一个实例来说明算子的可行性和有效性.     

一种基于集对分析的模糊多属性决策方法

针对属性权重和决策矩阵的属性值均为梯形模糊数的模糊多属性决策问题,提出了一种基于集对分析的决策方法.方法具有如下特点:通过借鉴集对分析理论和论域三划分的思想,把梯形模糊数属性值转化成联系数的形式,能有效处理决策过程中的不确定因素;对于权重向量和决策矩阵中的梯形模糊数采取不同的处理方法;用联系数决策理论的概念来刻画备选方案与正、负理想方案组成集对的同一对立程度;基于可能势的联系数排序能够准确反映联系数间的同一对立程度,方法直观,概念明确,易于实际操作.实例计算表明,方法是求解模糊多属性决策问题的一种有效工具.     

Nonlinear Level Set Learning for Function Approximation on Sparse Data with Applications to Parametric Differential Equations

A dimension reduction method based on the “Nonlinear Level set Learning” (NLL) approach is presented for the pointwise prediction of functions which have been sparsely sampled. Leveraging geometric information provided by the Implicit Function Theorem, the proposed algorithm effectively reduces the input dimension to the theoretical lower bound with minor accuracy loss, providing a one-dimensional representation of the function which can be used for regression and sensitivity analysis. Experiments and applications are presented which compare this modified NLL with the original NLL and the Active Subspaces (AS) method. While accommodating sparse input data, the proposed algorithm is shown to train quickly and provide a much more accurate and informative reduction than either AS or the original NLL on two example functions with high-dimensional domains, as well as two state-dependent quantities depending on the solutions to parametric differential equations.