基于剩余格理论的P-有界分配格的理想集代数

讨论P-有界分配格的理想集代数与剩余格的关系.证明了在适当选取蕴涵算子及相应的剩余算子之后,P-有界分配格的理想集代数就成为剩余格.定义了生成理想,并借助格论上的原子定义了P-有界分配格,然后讨论了它的一些性质,得到了一些好的结论.最后证明了P-有界分配格的理想集代数也是MV代数与R0代数.     

超空间上的选择算子理论与集值随机过程

本文首次引入了超空间(子集空间)上选择算子概念,给出了几类选择算子的存在定理。作为它们的应用,给出了集值随机变量同分布的选择刻画;圆满解决了依分布收敛集值随机变量列的向量值选择问题;研究了集值随机过程的正则选择与Markov选择,给出了集值Markov过程的离散化定理,证明了紧凸集值渐近鞅的向量值渐近鞅选择存在定理。     

Banach格上的b-Dunford-Pettis算子

为了进一步研究Banach格上算子的性质,受b-序有界集和Dunford-Pettis集定义的启发,给出了b-Dunford-Pettis算子的定义,研究了该算子与b-AM-紧算子(Dunford-Pettis全连续算子,弱极限算子,序Dunford-Pettis算子)间的关系;利用b-Dunford-Pettis算子与Dunford-Pettis算子的共轭关系,证明了b-Dunford-Pettis算子满足控制性.     

Banach空间中极限集与极限算子的弱化

为了进一步研究Banach空间中集合的紧致性,受极限集与极限算子定义的启发,给出了弱极限集与弱极限算子的定义,得到了它们的等价刻画,利用空间结构与算子理想的互动关系证明了弱极限算子全体是Pietsch意义下的闭满射算子理想．     

广义导算子的范数可达性

若B(H)表示希尔伯特空间H中所有有界线性算子之集.本文研究了定义在B(H)上的初等算子和广义导算子的范数可达性,证明了如果定义在B(H)中的初等算子和广义导算子是范数可达的,那么这些算子在B(H)中酉群上的限制也是范数可达的.     

R~n上一类高阶奇异Teodorescu算子的性质

讨论了R~n上的一类高阶奇异Teodorescu算子的有界性.首先定义了一类R~n上的高阶T算子,然后将算子分为两部分,并且利用Hlder不等式及一些引理证明了该算子在整个R~n上的一致有界性.     

基于Choquet积分的区间直觉梯形模糊多属性群决策

针对决策信息为区间直觉梯形模糊数(IVITFN)且属性间存在相互关联的多属性群决策问题,提出了基于Choquet积分理论的区间直觉梯形模糊关联平均(IVITFCA)算子.首先,基于IVITFN的运算法则和Choquet积分,定义了IVITFCA算子,并研究了该算子的相关性质.然后,提出了基于IVITFCA算子的多属性群决策方法.最后,通过供应商选择算例证明了所提方法的有效性与可行性.     

模糊可测函数

本文首先定义了模糊σ-域,算子～:■(X)→■(X)和σ:■(X)→■(X),证明了算子～与算子σ是可交的。然后利用集值分析知识讨论了模糊函数的逆函数,模糊函数的可测性以及它的可积性,导出了模糊函数可测、可积的充分必要条件。     

基于遗传算法的非规则墙体光伏阵列的设计

提出一种基于遗传算法的非规则墙体上光伏阵列的设计模型.提出适用于非规则墙体上光伏阵列的编解码算法,以及相对应的选择算子、交叉算子、变异算子,利用加权平均的方式对遗传算法中的适应度函数进行定义,实现对光伏阵列的发电量最大化和单位发电成本最小化.最后通过在Matlab环境下仿真,验证了本文算法的有效性.     

内积函数的表示

本文首先定义了内积函数,这个概念推广了内积的定义.然后定义了Hilbert空间（H,〈·,·〉）上由严格正算子A诱导的范数,这个范数与由〈·,·〉诱导的范数是等价的.进一步,证明了所有的内积函数与线性有界的严格正算子全体之间存在一一对应关系.     

Group decision making with expertons and uncertain generalized probabilistic weighted aggregation operators

Expertons and uncertain aggregation operators are tools for dealing with imprecise information that can be assessed with interval numbers. This paper introduces the uncertain generalized probabilistic weighted averaging (UGPWA) operator. It is an aggregation operator that unifies the probability and the weighted average in the same formulation considering the degree of importance that each concept has in the aggregation. Moreover, it is able to assess uncertain environments that cannot be assessed with exact numbers but it is possible to use interval numbers. Thus, we can analyze imprecise information considering the minimum and the maximum result that may occur. Further extensions to this approach are presented including the quasi-arithmetic uncertain probabilistic weighted averaging operator and the uncertain generalized probabilistic weighted moving average. We analyze the applicability of this new approach in a group decision making problem by using the theory of expertons in strategic management.     

The uncertain probabilistic OWA distance operator and its application in group decision making

In this paper, we present the uncertain probabilistic ordered weighted averaging distance (UPOWAD) operator. Its main advantage is that it uses distance measures in a unified framework between the probability and the OWA operator that considers the degree of importance of each concept in the aggregation. Moreover, it is able to deal with uncertain environments represented in the form of interval numbers. We study some of its main properties and particular cases such as the uncertain probabilistic distance (UPD) and the uncertain OWA distance (UOWAD) operator. We end the paper by presenting an application to a group decision making problem regarding the selection of robots.     

A novel hybrid genetic algorithm for solving Sudoku puzzles

In this article, a novel hybrid genetic algorithm is proposed. The selection operator, crossover operator and mutation operator of the genetic algorithm have effectively been improved according to features of Sudoku puzzles. The improved selection operator has impaired the similarity of the selected chromosome and optimal chromosome in the current population such that the chromosome with more abundant genes is more likely to participate in crossover; such a designed crossover operator has possessed dual effects of self-experience and population experience based on the concept of tactfully combining PSO, thereby making the whole iterative process highly directional; crossover probability is a random number and mutation probability changes along with the fitness value of the optimal solution in the current population such that more possibilities of crossover and mutation could then be considered during the algorithm iteration. The simulation results show that the convergence rate and stability of the novel algorithm has significantly been improved.     

Absolutely Summing Processes

Abstract

Absolutely summing processes are defined, which form a subclass of weakly operator harmonizable processes. When the parameter space is the set of real numbers, it is proved that an absolutely summing process is represented as an integral of operator stationary processes with respect to an appropriate probability measure. To do this, weak convergence of scalar and vector measures is considered. Then we prove compactness of the unit ball of vector measures under certain topologies, and we apply the Choquet theorem to derive an integral representation.     

Distance measures with heavy aggregation operators

The use of distance measures and heavy aggregations in the ordered weighted averaging (OWA) operator is studied. We present the heavy ordered weighted averaging distance (HOWAD) operator. It is a new aggregation operator that provides a parameterized family of aggregation operators between the minimum distance and the total distance operator. Thus, it permits to analyze an aggregation from its usual average (normalized distance) to the sum of all distances available in the aggregation process. We analyze some of its main properties and particular cases such as the normalized Hamming distance, the weighted Hamming distance and the OWA distance (OWAD) operator. This approach is generalized by using quasi-arithmetic means obtaining the quasi-arithmetic HOWAD (Quasi-HOWAD) operator and with norms obtaining the heavy OWA norm (HOWAN). Further extensions to this approach are presented by using moving averages forming the moving HOWAD (HOWMAD) and the moving Quasi-HOWAN (Quasi-HOWMAN) operator. The applicability of the new approach is studied in a decision making model regarding the selection of national policies. We focus on the selection of monetary policies. The key advantage of this approach is that we can consider several sources of information that are independent between them.     

一类混合遗传算法的收敛性研究

针对遗传算法的有效性一般是通过数值实验来说明这一问题,本文在三个假设的条件下给出了一类混合遗传算法依概率收敛的证明,而且得到了在此算法的框架下通过改变子种群Ⅱ的变异算子而不改变依概率收敛特性的结论.     

A hybrid immune multiobjective optimization algorithm

In this paper, we develop a hybrid immune multiobjective optimization algorithm (HIMO) based on clonal selection principle. In HIMO, a hybrid mutation operator is proposed with the combination of Gaussian and polynomial mutations (GP-HM operator). The GP-HM operator adopts an adaptive switching parameter to control the mutation process, which uses relative large steps in high probability for boundary individuals and less-crowded individuals. With the generation running, the probability to perform relative large steps is reduced gradually. By this means, the exploratory capabilities are enhanced by keeping a desirable balance between global search and local search, so as to accelerate the convergence speed to the true Pareto-optimal front in the global space with many local Pareto-optimal fronts. When comparing HIMO with various state-of-the-art multiobjective optimization algorithms developed recently, simulation results show that HIMO performs better evidently.     

梯形直觉模糊数集成算子及在决策中的应用研究

在直觉模糊集理论基础上,用梯形模糊数表示直觉模糊数的隶属度和非隶属度,进而提出了梯形直觉模糊数;然后定义了梯形直觉模糊数的运算法则,给出了相应的证明,并基于这些法则,给出了梯形直觉模糊加权算数平均算子(TIFWAA)、梯形直觉模糊数的加权二次平均算子(TIFWQA)、梯形直觉模糊数的有序加权二次平均算子(TIFOWQA)、梯形直觉模糊数的混合加权二次平均算子(TIFHQA)并研究了这些算子的性质;建立了不确定语言变量与梯形直觉模糊数的转化关系,并证明了转化的合理性;定义了梯形直觉模糊数的得分函数和精确函数,给出了梯形直觉模糊数大小比较方法;最后提供了一种基于梯形直觉模糊信息的决策方法,并通过实例结果证明了该方法的有效性。     

Mathematical Theory of Phenotypical Selection

A general concept of phenotypical structure over a genotypical structure is developed. The direct decompositions of multilocus phenotypical structures are considered. Some aspects of phenotypical heredity are described in terms of graph theory. The acyclic phenotypical structures are introduced and studied on this base. The evolutionary equations are adjusted to the phenotypical selection. It is proved that if a phenotypical structure is acyclic then the set of fixed points of the corresponding evolutionary operator is finite except for a proper algebraic subset of the operator space. Some applications of this theorem are given.     

Concave group methods for variable selection and estimation in high-dimensional varying coefficient models

The varying-coefficient model is flexible and powerful for modeling the dynamic changes of regression coefficients. We study the problem of variable selection and estimation in this model in the sparse, high-dimensional case. We develop a concave group selection approach for this problem using basis function expansion and study its theoretical and empirical properties. We also apply the group Lasso for variable selection and estimation in this model and study its properties. Under appropriate conditions, we show that the group least absolute shrinkage and selection operator (Lasso) selects a model whose dimension is comparable to the underlying model, regardless of the large number of unimportant variables. In order to improve the selection results, we show that the group minimax concave penalty (MCP) has the oracle selection property in the sense that it correctly selects important variables with probability converging to one under suitable conditions. By comparison, the group Lasso does not have the oracle selection property. In the simulation parts, we apply the group Lasso and the group MCP. At the same time, the two approaches are evaluated using simulation and demonstrated on a data example.