物元可拓集集合性质研究

在可拓集合概念基础上,提出了向量可拓集和区间可拓集概念,研究了物元可拓集合的交、并、求补等集合运算及其性质.     

可拓营销理论研究

利用物元理论、事元理论和可拓集合理论 ,对需要、产品、市场、资源、企业等进行可拓分析 ,提出可拓营销的基本理论——产品开拓规律、可拓市场、可拓资源、健全企业等     

航空备件可拓聚类分析

应用可拓学的基本理论与方法 ,分析了航空备件的数值特征 ,建立了航空备件的物元模型 ;应用可拓原理对传统的聚类分析方法做了拓展 ,并据此对航空备件集合进行了划分 ,为航空备件的分类管理提供了一种崭新的方法 .     

一类物元可拓集的性质研究

提出了物元等价类概念,并利用子集合X和属性子集R对物元集合S(U,A,V,f)在集合论域U上构造了一类可拓集合A~,并讨论了A~关于X和R的一些相关性质.     

可拓目标规划方法及其应用

本文基于可拓数学和物元分析理论，介绍了可拓集合、关联函数和可拓满意点等概念，提出了可拓优化的概念，建立了可拓目标规划模型，给出了可拓目标规划方法的算法．实例表明，可拓目标规划方法有一定的实用性．     

随机事件与随机变量概率分布的拓展研究

给出了随机事元的拓展概率以及随机事元可拓集的概念.运用可拓集合、可拓变换与可拓推理等可拓学的理论与方法,对随机事件发生的概率与随机变量概率分布的变化作了初步的拓展研究.     

基于可拓评价的无菌医疗器具生产环境安全评价与预警研究

将可拓评价方法用于无菌医疗器具生产环境安全评价与预测研究,建立了无菌医疗器具生产环境安全可拓物元评价模型.利用关联函数动态赋权法,改进了待评物元所属安全等级特征值的计算方法,使得判别更加准确.同时也对各单项指标进行了评价与预测,可为企业决策者提供更加有效的科学依据.     

可拓集合及其应用研究

介绍了扩展的可拓集合概念 ,提出了可拓集合论需要进一步研究的内容 ,并综述了可拓集合在人工智能、市场、资源、检测和控制等领域的应用 .     

可拓集联系数的势及其关系

在可拓集联系数的基础上,提出了可拓集集对势的概念,并且对集合型可拓集联系数的关系做了定义.这使得可拓集合与集对分析的结合更加紧密,而且可以在实际中得到更好的应用.     

关于可拓集合的运算

根据蔡文提出的可拓集合的新定义,给出了可拓集合的包含、并、交、非运算的新定义,并讨论了有关运算性质,进而获得可拓域与稳定域的几个交并运算结果.     

基于可拓集的可拓分类知识获取研究

以可拓集理论为依据,给出基于可拓变换的可拓分类知识的定义,并在信息元集和评价信息元集的基础上,探讨可拓分类知识的获取方法,包括质变域知识的获取、量变域知识的获取和有关拓界的知识的获取.这是可拓数据挖掘的主要内容之一,为从数据库中获取变化的分类知识提供了新的思路.     

直觉模糊集的扩展原理

基于文[1]给出的直觉模糊集的截集、分解定理和表现定理,利用模糊集的扩展原理,本文建立了直觉模糊集的扩展原理.首先,给出了直觉模糊集的扩展原理及其等价形式;其次,讨论了直党模糊集的扩展原理的有关性质;最后,研究了复合函数的直觉模糊集扩展原理及其性质.     

Group Theory,Computational Thinking,and Young Mathematicians

In this article, we investigate the artistic puzzle of designing mathematics experiences (MEs) to engage young children with ideas of group theory, using a combination of hands-on and computational thinking (CT) tools. We elaborate on: (1) group theory and why we chose it as a context for young mathematicians’ experiences with symmetry and transformations; (2) our ME design principles of agency, access, surprise and audience; (3) the affordances of CT that complement our design principles; and (4) three ME variations we tested in grades 3–6 classrooms. We then reflect on the ME variations based on our design principles and the affordances of CT, and consider how the MEs may be further adapted and improved.     

A new efficient algorithm based on DC programming and DCA for clustering

In this paper, a version of K-median problem, one of the most popular and best studied clustering measures, is discussed. The model using squared Euclidean distances terms to which the K-means algorithm has been successfully applied is considered. A fast and robust algorithm based on DC (Difference of Convex functions) programming and DC Algorithms (DCA) is investigated. Preliminary numerical solutions on real-world databases show the efficiency and the superiority of the appropriate DCA with respect to the standard K-means algorithm.      

Extension principles for interval-valued intuitionistic fuzzy sets and algebraic operations

The Atanassov’s intuitionistic fuzzy (IF) set theory has become a popular topic of investigation in the fuzzy set community. However, there is less investigation on the representation of level sets and extension principles for interval-valued intuitionistic fuzzy (IVIF) sets as well as algebraic operations. In this paper, firstly the representation theorem of IVIF sets is proposed by using the concept of level sets. Then, the extension principles of IVIF sets are developed based on the representation theorem. Finally, the addition, subtraction, multiplication and division operations over IVIF sets are defined based on the extension principle. The representation theorem and extension principles as well as algebraic operations form an important part of Atanassov’s IF set theory.     

Estimation and status prediction in a discrete mover‐stayer model with covariate effects on stayer's probability

A discrete‐time mover‐stayer (MS) model is an extension of a discrete‐time Markov chain, which assumes a simple form of population heterogeneity. The individuals in the population are either stayers, who never leave their initial states or movers who move according to a Markov chain. We, in turn, propose an extension of the MS model by specifying the stayer's probability as a logistic function of an individual's covariates. Such extension has been recently discussed for a continuous time MS but has not been considered before for a discrete time one. This extension allows for an in‐sample classification of subjects who never left their initial states into stayers or movers. The parameters of an extended MS model are estimated using the expectation‐maximization algorithm. A novel bootstrap procedure is proposed for out of sample validation of the in‐sample classification. The bootstrap procedure is also applied to validate the in‐sample classification with respect to a more general dichotomy than the MS one. The developed methods are illustrated with the data set on installment loans. But they can be applied more broadly in credit risk area, where prediction of creditworthiness of a loan borrower or lessee is of major interest.     

Bounds for a multivariate extension of range over standard deviation based on the Mahalanobis distance

The range over standard deviation of a set of univariate data points is given a natural multivariate extension through the Mahalanobis distance. The problem of finding extrema of this multivariate extension of “range over standard deviation” is investigated. The supremum (maximum) is found using Lagrangian methods and an interval is given for the infinimum. The independence of optimizing the Mahalanobis distance and the multivariate extension of range is demonstrated and connections are explored in several examples using an analogue of the “hat” matrix of linear regression.