Interval grey number sequence prediction by using non-homogenous exponential discrete grey forecasting model

This paper aims to study a new grey prediction approach and its solution for forecasting the main system variable whose accurate value could not be collected while the potential value set could be defined. Based on the traditional nonhomogenous discrete grey forecasting model（NDGM）, the interval grey number and its algebra operations are redefined and combined with the NDGM model to construct a new interval grey number sequence prediction approach. The solving principle of the model is analyzed, the new accuracy evaluation indices, i.e. mean absolute percentage error of mean value sequence（MAPEM） and mean percent of interval sequence simulating value set covered（MPSVSC）, are defined and, the procedure of the interval grey number sequence based the NDGM（IG-NDGM） is given out. Finally, a numerical case is used to test the modelling accuracy of the proposed model. Results show that the proposed approach could solve the interval grey number sequence prediction problem and it is much better than the traditional DGM（1,1） model and GM（1,1） model.

Prediction model of interval grey number based on DGM(1,1)

In grey system theory,the studies in the field of grey prediction model are focused on real number sequences,rather than grey number ones.Hereby,a prediction model based on interval grey number sequences is proposed.By mining the geometric features of interval grey number sequences on a two-dimensional surface,all the interval grey numbers are converted into real numbers by means of certain algorithm,and then the prediction model is established based on those real number sequences.The entire process avoids the algebraic operations of grey number,and the prediction problem of interval grey number is usefully solved.Ultimately,through an example＇s program simulation,the validity and practicability of this novel model are verified.

CALCULATION FOR KERNEL OF INTERVAL GREY NUMBER BASED ON BARYCENTER APPROACH

The kernel of interval grey number is most likely the real number,which can be used to represent whitenization value of interval grey number.A novel method for calculating kernel of interval grey number is constructed based on the geometric barycenter of whitenization weight function in the two-dimensional coordinate plane,and the calculation of kernel is converted to the calculation of barycenter in geometric figures.The method fully considers the effect of all information contained in whitenization weight function on the calculation result of kernel,and is the extension and perfection of the existing methods in the scope of application.

Dynamic Multi-Attribute Decision Analysis on Three-Parameter Interval Grey Number of Upper(Lower)Deviation Quasi-Normal Distribution

Aiming at the dynamic multi-attribute decision making problem where the weight of each decision stage and attribute weight are completely unknown and the attribute value is unknown distributed three-parameter interval grey number,a threeparameter interval grey number dynamic multiattribute grey target decision making method with attribute value following quasi-normal distribution is proposed.Firstly,the position relationship between the“center of gravity”point and the kernel of the threeparameter interval grey number is discussed.According to the characteristicthat the attribute value obeys the quasi-normal distribution,anew weight isgiventothe“centerof gravity”point,and a new distance measure formula of the three-parameter interval grey number is defined.Secondly,according to the principle of maximum entropy,the objective programming model is constructed to determine the stage weight and attributeweight.Then,the schemes aresorted according to thesize of the comprehensive bull's-eye distance Finally an example is given to illustrate the effectiveness of the decision model.

Grey clustering evaluation based on AHP and interval grey number

Purpose–The purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process(AHP)and interval grey number(IGN)to solve the clustering evaluation problem with IGNs.Design/methodology/approach–First,the centre-point triangular whitenisation weight function with real numbers is built,and then by using interval mean function,the whitenisation weight function is extended to IGNs.The weights of evaluation indexes are determined by AHP.Finally,this model is used to evaluate the flight safety of a Chinese airline.The results indicate that the model is effective and reasonable.Findings–When IGN meets certain conditions,the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative.It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.Originality/value–The traditional grey clustering model is extended to the field of IGN.It can make full use of all the information of the IGN,so the result of the evaluation is more objective and reasonable,which provides supports for solving practical problems.

An improved interval model updating method via adaptive Kriging models

Interval model updating(IMU)methods have been widely used in uncertain model updating due to their low requirements for sample data.However,the surrogate model in IMU methods mostly adopts the one-time construction method.This makes the accuracy of the surrogate model highly dependent on the experience of users and affects the accuracy of IMU methods.Therefore,an improved IMU method via the adaptive Kriging models is proposed.This method transforms the objective function of the IMU problem into two deterministic global optimization problems about the upper bound and the interval diameter through universal grey numbers.These optimization problems are addressed through the adaptive Kriging models and the particle swarm optimization(PSO)method to quantify the uncertain parameters,and the IMU is accomplished.During the construction of these adaptive Kriging models,the sample space is gridded according to sensitivity information.Local sampling is then performed in key subspaces based on the maximum mean square error(MMSE)criterion.The interval division coefficient and random sampling coefficient are adaptively adjusted without human interference until the model meets accuracy requirements.The effectiveness of the proposed method is demonstrated by a numerical example of a three-degree-of-freedom mass-spring system and an experimental example of a butted cylindrical shell.The results show that the updated results of the interval model are in good agreement with the experimental results.

Novel combinatorial algorithm for the problems of fuzzy grey multi-attribute group decision making

To study the fuzzy and grey information in the problems of multi-attribute group decision making, the basic concepts of both fuzzy grey numbers and grey interval numbers are given firstly, then a new model of fuzzy grey multi-attribute group decision making based on the theories of fuzzy mathematics and grey system is presented. Furthermore, the grey interval relative degree and deviation degree is defined, and both the optimistic algorithm of the grey interval relational degree and the algorithm of deviation degree minimization for solving this new model are also given. Finally, a decision making example to demonstrate the feasibility and rationality of this new method is given, and the results by using these two algorithms are uniform.

Fundamental definitions and calculation rules of grey mathematics: a review work

Grey mathematics is the mathematical foundation of the grey system theory. Recently, some important results have been achieved. In order to accelerate the development of grey mathematics, the results are summarized and redefined. This paper includes the fundamental definitions and calculation rules of the grey hazy set, grey number, grey matrix and grey function. Grey mathematics includes four types of operation, i.e. the grey operation, the whitened operation, the covered operation and the only potential true operation. According to its intrinsic quality, the covered operation, which differs from the interval one, is called as the whole-proximate calculation that means the proximate calculation spreads through the whole range of the covered set of every grey number, and we confirm that it may be a new branch of computational or applied mathematics. The overview should develop the grey system theory and grey mathematics.

基于核和灰度的区间灰数运算法则

区间灰数运算是灰色系统的理论基础,对灰色系统理论发展的影响举足轻重。首先给出了灰数"核"的定义,基于"核"和灰数灰度建立了区间灰数运算公理、运算法则和新的灰代数系统,并研究了运算性质。至此,灰数运算被转化为实数运算,区间灰数运算的难题在一定程度上得到解决。定义的区间灰数运算可以推广到以区间灰数为基本元素的灰色代数方程、灰色微分方程、灰色矩阵运算的情形,而且有助于由于受到区间灰数运算困难的制约一直进展缓慢的灰色投入产出和灰色规划等方面的研究。

基于核与灰度的区间灰数运算法则的修正

针对灰度不减公理的局限性,对基于核和灰度的灰数加、减和数乘运算法则进行了修正。定理2和实例表明修正后的灰数运算法则更具合理性,并给出了新的灰数运算法则在多属性决策中的应用。

基于Choquet积分的区间灰数多属性决策方法

针对多属性决策中属性间的关联性对决策结果的影响,提出了基于区间灰数灰度和Choquet积分的区间灰数多属性决策方法。根据区间灰数灰度的定义,证明了区间灰数的灰度是一种灰测度,由此定义了关于区间灰数灰度的离散Choquet积分并证明其性质;依据各方案的综合属性值的方差最小的原则建立多目标优化模型,确定了各属性或属性集的权重,最后利用Choquet积分对各方案的效果评价向量进行集成。通过实例说明了该方法的有效性。

灰数灰度的一种公理化定义

基于对已有的几种灰数灰度定义的讨论 ,建立了灰数灰度定义的公理系统 ;以灰数灰度定义公理为准绳 ,由灰数产生的背景或论域及灰数取数域的测度构造出一种新的灰数灰度定义式。新定义克服了原有定义中存在的问题 。

基于核和灰度的区间灰数行列式的若干性质

为了简化区间灰数行列式的运算,完善区间灰数的运算和灰数代数系统的理论基础,利用区间灰数的简化形式探讨区间灰数行列式的性质,得到了基于核和灰度的区间灰数行列式的若干性质,简化了区间灰数行列式的运算,为进一步探讨区间灰数矩阵及区间灰数线性方程组奠定了基础.

区间灰系列的熵度量及其应用(英文)

研究了灰信息理论的不确定性度量方法及其在决策中的应用,说明了建立在有限区间灰数序列基础上的灰序列熵与Shannon概率熵是不同的。讨论了灰数的度量公式和性质,包括不变性、应用条件、两个灰数并与交的灰熵等。最后给出一个应用实例验证了该方法的有效性。

Study of evolution forecast of the area of the Huanghe River mouth

From 1979 to 1989, the current Qingshuigou course of the Huanghe River formed a sub - delta which resembles a beak extending into the Laizhou Bay. It covers 618 km2 in area. To meet the needs of developing and constructing the Huanghe River Delta and under the presupposition of keeping the current course for 15-20 a, we forecast mainly by using the OM (1, 1) model that the front border of the sub-delta will be close to 119°30'E and its area will become 923 km2by the end of the year 2000. The Huanghe River will make land 760 km2 in area.

基于核和灰度的区间灰数的序关系

首先,根据区间灰数的核的概念,在同核区间灰数定义的基础上,讨论了基于核的区间灰数的等价关系和大小关系,并给出了一系列相关结论。其次,根据区间灰数的灰度的概念,在同灰度区间灰数定义的基础上,讨论了基于灰度区间灰数的等价关系和大小关系,也得到了一系列相关结论。最后,在同时考虑核和灰度的基础上,讨论了区间灰数的序关系,并得到区间灰数的偏序关系不是全序关系的结论,从而说明基于核和灰度的区间灰数代数系统不能定义大小关系,同时这也说明了基于核和灰度的灰数表示形式具有一定的局限性。

区域旱灾风险管理中的灰色局势群决策方法

为提高区域旱灾风险管理中决策信息的准确性和合理性,提出了一种基于灰色不确定语言变量的局势群决策方法,并将其应用到河南省农业旱灾风险管理中。首先,给出灰色不确定语言变量的定义和运算规则;然后综合考虑决策群体一致性、语言信息灰度和专家主观权重提出专家综合权重的确定方法,并给出基于局势偏差的目标权重确定方法;最后通过求解决策专家评价下的综合效果样本值矩阵,确定最优灰色局势。实例分析表明了所提出方法的有效性,并为区域农业旱灾风险管理提供参考。

基于灰数灰度的土壤有机质高光谱估测

为克服光谱估测中的不确定性,利用统计回归与灰色系统理论建立土壤有机质高光谱估测模型。以山东省济南市章丘区采集的76个土壤样本为研究对象,首先对土壤光谱数据进行变换处理,根据极大相关性原则选取特征波段的估测因子,建立各特征波段的一元线性回归预测模型;其次,对各估测因子进行由小到大排序,计算估测因子排序后的土壤有机质含量的滑动方差,将滑动方差转化为灰数的灰度值,并将其用于修正估测因子,然后再建立各特征波段的一元线性回归修正模型;最后采用平均法与加权法融合各个单波段的预测值。结果表明,所建估测模型精度和检验精度均显著提高,13个检验样本的R^(2)=0.911,MRE=7.764%。研究表明,本文建立的基于灰数灰度的土壤有机质含量高光谱估测模型是可行有效的。

A New DGM(1,1)Model with a Grey Parameter and Its Application

In the DGM(1, 1) model modeling process, the influencing factors are uncertain. But the solution of DGM(1, 1) model with uncertain information is unique, which conflicts with the nonuniqueness principle of solution in grey theory. In view of this situation, this paper makes an in-depth analysis of the meaning of grey action quantity β_(2) in DGM(1, 1) model and regards β_(2) as an interval grey number. The maximum possibility whitenization value is given to estimate the kernel of grey number,and the typical possibility function is constructed to describe the possibility of grey number taking different values. A new DGM(1, 1) model with a grey parameter is then proposed, whose simulation results are interval grey numbers. The proposed model is compatible with the DGM(1, 1) model in model structure and simulation results. Finally, the practical example results show the applicability and effectiveness of the proposed model.

Environmental stress level evaluation approach based on physical model and interval grey association degree

Associating environmental stresses （ESs） with built-in test （BIT） output is an important means to help diagnose intermittent faults （IFs）. Aiming at low efficiency in association of traditional time stress measurement device （TSMD）, an association model is built. Thereafter, a novel approach is given to evaluate the integrated environmental stress （IES） level. Firstly, the selection principle and approach of main environmental stresses （MESs） and key characteristic parameters （KCPs） are presented based on fault mode, mechanism, and ESs analysis （FMMEA）. Secondly, reference stress events （RSEs） are constructed by dividing IES into three stress levels according to its impact on faults; and then the association model between integrated environmental stress event （IESE） and BIT output is built. Thirdly, an interval grey association approach to evaluate IES level is proposed due to the interval number of IES value. Consequently, the association output can be obtained as well. Finally, a case study is presented to demonstrate the proposed approach. Results show the proposed model and approach are effective and feasible. This approach can be used to guide ESs measure, record, and association. It is well suited for on-line assistant diagnosis of faults, especially IFs.