P-集合的格论性质

P-集合是把动态特性引入到有限普通集合中,改进普通集合得到的,P-集合是由内P-集合(internal packet sets)与外P-集合(outer packet sets)构成的集合对.利用P-集合理论,给出只集合的依赖关系和度量,证明P-集合在依赖关系下构成格,进而给出基于格论的P-.集合的相关性质.     

基于层次内P-集合的粒计算模型

在层次内P-集合基础上,给出层次内P-集合的粒度,层次内P-集合的粒度计算,从粒的角度研究层次内P-集合之间的关系.把普通P-集合扩展为层次P-集合,并研究层次P-集合的粒计算特征.     

内-遗传信息与它的内P-推理发现特征

P-集合(Packet sets)是由内P-集合X~F(internal packet setsX~F)与外P-集合X~F(outer packet setsX~F)构成的集合对;或者,(X~F,X~F)是P-集合.给定有限普通集合X={x_1,x_2,…,x_q},α={α_1,α_2,…,α_k}是X的属性集合;若在α内补充属性,则X变成内P-集合X~F={x_1,x_2,…,x_p},X内元素x_1,x_2,…,x_p被内-遗传到X~F内,P≤q,P,q∈N~+.内-遗传是P-集合的重要应用特征之一.利用内P-集合,给出内-遗传信息概念,内-遗传信息的遗传特征;利用内P-推理,给出内-遗传信息的内P-推理辨识与未知内-遗传信息的内P-推理发现.     

函数内P-集合的副集及其区间生成规律

基于函数P-集合(S~F,S~F)的动态性、规律性,提出函数内P-集合的副集,给出函数内P-集合副集的区间生成结构、区间生成规律,给出内P-规律ω~F的区间拆分规律及其拆分度量,解决了函数内P-集合S~F状态规律受游弋于S~F边缘的元素(函数内P-集合的副集中的函数)的干扰,而呈现出来的动态规律(区间拆分规律)以及动态变化程度(拆分度量)的刻画等问题.最后以实例分析函数内P-集合副集及其区间生成规律在风险投资中的应用.     

层次内P-集合及其性质

P-集合是由内P-集合X~F(internal packet setsX~F)与外P-集合X~F(outerpacket setsX~F)构成的集合对,或者(X~F,X~F)是P-集合.利用P-集合理论,给出内P-集合的扩展模型—层次内P-集合,把内P-集合的依赖关系扩展到层次内P-集合中,并研究层次内P-集合的性质.层次内P-集合是普通内P-集合的扩展,提供了多角度、多层次分析和研究问题的方法.     

P-集合与双信息规律生成

P-集合(pacdet sets)是由内P-集合X~F(internal packet set X~F)与外P-集合X~F(outer packet set X~F)构成的集合对.P-集合是把动态特性引入到普通集合中,利用普通集合(cantor set)被提出的.在一定的条件下,P-集合能够回到普通集合的原点.利用P-集合,给出双信息规律生成概念,给出双信息规律依赖的属性特征,提出双信息规律生成定理,辨识定理,给出应用.P-集合是研究动态信息系统的一个新的数学理论与数学方法.     

内P-反动态信息特征与信息恢复

P-集合(packet sets)是一个动态模型,P-集合是由内P-集合x~F(internal packet set X~F)与外P-集合XF(~Fouter packet set X~F)构成的元素集合对;或者(X~F,X~F)是P-集合.利用内P-集合的结构,给出内P-信息,内P-反动态信息,信息的内P-反动态恢复概念,给出内P-反动态信息的属性合取收缩生成,给出内P-反动态信息与内P-信息同属性定理,给出内P-反动态信息存在与属性合取范式定理,给出信息的内P-反动态恢复属性定理.这些基本理论结果是把内P-集合与一类信息系统故障状态识别交叉,渗透研究得到的.     

内扰动与属性合取扩展规律挖掘-发现

扰动的存在使得系统标准输出规律变成非标准输出规律的现象等价于在扰动存在的条件下,非标准输出规律被挖掘-发现;利用函数P-集合模型与它的动态特征给出这个现象的理论研究.主要结果:给出函数P-集合的结构与规律扰动,扰动恢复概念;给出扰动度量;利用这些概念,给出内扰动与属性合取扩展定理;属性合取扩展与内扰动规律挖掘定理,内扰动与规律挖掘辨识定理,内扰动与规律挖掘属性不变性定理.     

外P-信息融合和它的属性合取特征

P-集合(packet sets)是由内P-集合X~F(internal packet set X~F)与外P-集合X~F(outer packet set X~F)构成的集合对;或者,(X~F,X~F)是P-集合.利用外P-集合,给出外P-信息融合生成,外P-信息融合补充生成与外P-信息融合度量概念;给出外P-信息融合生成定理,外P-信息融合依赖定理;给出外P-信息融合还原定理;给出外P-信息融合的属性合取定理与属性合取压缩定理;给出属性合取压缩外P-信息融合发现原理.     

新集合概念的应用与学生创新思维的培养

首先阐述了模糊集合、粗集合与P-集合(P=Packet)的概念、结构、特征及其图形的直观表示,此三种集合在本文中将简称"新集合".然后介绍了新集合与有限普通元素集合的关系,介绍了新集合的简单应用.教学实践表明,将这些新集合概念渗透到基础数学教学中,对培养学生的创新思维能力与研究开发能力具有十分有益的促进作用.     

Application of multilevel analysis in animal sciences

Multilevel modeling is considerably useful way to analyze hierarchical data sets. The main purpose of this paper is to apply multilevel analysis in animal science and also show that this modeling technique is appropriate to analyze this kind of data. Thus multilevel modeling technique is used to analyze the milk yield data which has hierarchical structures, sires nested within cows. As a result of the analysis done in this paper, it is obvious that multilevel modeling is needed to use for analyzing this data. This illustrates that it is a convenient way to use multilevel analysis for the data which obtained from animals when the data have hierarchies.     

Hierarchical forecasting based on AR-GARCH model in a coherent structure

This paper compares the accuracy of the aggregate forecasting with the bottom-up forecasting based on AR-GARCH model for the return rate of simulated Dow Jones Industrial Average. Most of the existing stock price index studies did not consider the hierarchical structure and often missed the coherent relationships between individual components. In this experiment, we simulated 30 coherent components based on AR(2)-GARCH(1, 1) model. Then we evaluated the performance of both forecasting methods ignoring the coherent structure. The results of our experiment indicated that the accuracy of forecasting method varied depending on the correlation degree of 30 coherent components, however the data noise did not significantly influenced the performance of hierarchical forecasting method.     

A hierarchical location-allocation model with travel based on expected referral distances

The hierarchical p-median location-allocation model assumes that patrons always travel to the closest facility of appropriate level and that their interests are best served when the distances they must travel to do this are minimized. This assumption about travel behavior is unrealistic, patrons in the real world are known, for instance, to bypass lower level facilities that can serve their needs to attend higher level facilities. We introduce the concept of “expected distance under referral” to deal with such irrationality and incorporate it into a location-allocation model that minimizes the negative effects of such irrational behavior. We demonstrate the model with several types of non-optimal travel behavior.     

Hierarchical Archimedean copulas through multivariate compound distributions

In this paper, we propose a new hierarchical Archimedean copula construction based on multivariate compound distributions. This new imbrication technique is derived via the construction of a multivariate exponential mixture distribution through compounding. The absence of nesting and marginal conditions, contrarily to the nested Archimedean copulas approach, leads to major advantages, such as a flexible range of possible combinations in the choice of distributions, the existence of explicit formulas for the distribution of the sum, and computational ease in high dimensions. A balance between flexibility and parsimony is targeted. After presenting the construction technique, properties of the proposed copulas are investigated and illustrative examples are given. A detailed comparison with other construction methodologies of hierarchical Archimedean copulas is provided. Risk aggregation under this newly proposed dependence structure is also examined.     

分层变分包含问题中经由分层不动点途径的黏性方法

基于一原创技术—指标选取法,以黏性法分层逼近可数族非线性映射之公共不动点.并于适当条件下得一强收敛定理,用以解决Hilbert空间背景下之分层变分包含问题.     

A top-down hierarchical classification method

In this paper we present an original top-down hierarchical classification algorithm. In our approach we associate to each decomposition a ‘degree of separability’ which is used to evaluate the efficiency of the decomposition. In particular, it shows whether or not the classes are well separated and also whether or not they are homogeneous. This algorithm can be applied to items defined by real and bounded numbers.     

A binary particle swarm optimization algorithm inspired by multi-level organizational learning behavior

Recently, nature-inspired algorithms have increasingly attracted the attention of researchers. Due to the fact that in BPSO the position vectors consisting of ‘0’ and ‘1’ can be seen as a decision behavior (support or oppose), in this paper, we propose a BPSO with hierarchical structure (BPSO_HS for short), on the basis of multi-level organizational learning behavior. At each iteration of BPSO_HS, particles are divided into two classes, named ‘leaders’ and ‘followers’, and different evolutionary strategies are used in each class. In addition, the mutation strategy is adopted to overcome the premature convergence and slow convergent speed during the later stages of optimization. The algorithm was tested on two discrete optimization problems (Traveling Salesman and Bin Packing) as well as seven real-parameter functions. The experimental results showed that the performance of BPSO_HS was significantly better than several existing algorithms.     

A hierarchical copula-based world-wide valuation of sovereign risk

We propose a new model for the aggregation of risks that is very flexible and useful in high dimensional problems. We propose a copula-based model that is both hierarchical and hybrid (HYC for short), because: (i) the dependence structure is modeled as a hierarchical copula, (ii) it unifies the idea of the clusterized homogeneous copula-based approach (CHC for short) and its limiting version (LHC for short) proposed in Bernardi and Romagnoli (2012, 2013). Based on this, we compute the loss function of a world-wide sovereign debt portfolio which accounts for a systemic dependence of all countries, in line with a global valuation of financial risks. Our approach enables us to take into account the non-exchangeable behavior of a sovereign debts’ portfolio clustered into several classes with homogeneous risk and to recover a possible risks’ hierarchy. A comparison between the HYC loss surface and those computed through a pure limiting approach, which is commonly used in high dimensional problems, is presented and the impact of the concentration and the granularity errors is appreciated. Finally the impact of an enlargement of the dependence structure is discussed, in the contest of a geographical area sub-portfolios analysis now relevant to determine the risk contributions of subgroups under the presence of a wider dependence structure. This argument is presented in relation to the evaluation of the insurance premium and the collateral related to the designed project of an euro-insurance-bond.     

Efficient and reliable hierarchical error estimates for an elliptic obstacle problem

We present and analyze novel hierarchical a posteriori error estimates for a self-adjoint elliptic obstacle problem. Under a suitable saturation assumption, we prove the efficiency and reliability of our hierarchical estimates. The proof is based upon some new observations on the efficiency of some hierarchical error indicators. These new observations allow us to remove an additional regularity condition on the underlying grid required in the previous analysis. Numerical computations confirm our theoretical findings.