Grey dynamic input-output analysis

A system whose information is partially known and partially unknown is named as a grey system and it is general situation in reality. On the basis of grey number/matrix and their covered operation, some definitions of grey functions are enumerated. Combining grey system theory with traditional dynamic input-output analysis, we propose the grey dynamic input-output analysis. It has two types, i.e., the continuous and the disperse. The most important results are the computational formulas of the matrix-covered set of inverse grey investment coefficient matrix. By using them, we can forecast and control the economic system under the uncertain situation. For the calculation of the matrix-covered set of inverse grey matrices, we obtain the desired formulas, which can be applied to general grey matrices. The formulas can greatly promote the development of grey theory and enrich the applied fields of uncertain mathematics. The modified case illustrates our method.     

The foundation of the grey matrix and the grey input–output analysis

The grey systems theory aims at the objects that their information is inadequate and this situation is general in reality. It has been urgent work to study the uncertain problems using the missing information. With the help of the simple introduction of grey systems theory, we further study the covered operation and get some calculation rules about grey number. The definition of grey matrix (GM) and its covered operation are proposed. Particularly, some results of the inverse grey matrix are obtained. Also with the help of the proposed grey matrix theory and the traditional input–output analysis, we propose the grey input–output analysis. The most important results are the computational formulas and their rigorous proofs of the matrix-covered set of the inverse grey Leontief coefficient’s matrix. It provides an effective tool to study an economic system by the input–output analysis under the uncertain situation. The modified case verifies the effectiveness of our methodology.     

关于粗糙集和灰色系统之间某些关系的探讨

首先介绍粗糙集与灰色系统两种理论,并对二者进行比较。接着介绍普通粗糙集、P-粗糙集以及灰色集的定义,并就灰色集、模糊集和经典集合三者进行对比分析。我们提出了点灰度和集灰度两种灰度概念用于描述灰色系统的信息不确定性。通过P粗糙集导出相应的灰色集,并研究相关的灰度、粗糙度与边界域的性质和关系。分析表明使用导出的灰色集对系统的信息不确定性的估计与相应的粗糙集是一致的,因此两种理论在描述和处理不确定性信息系统方面的一定的相关性,将两种理论相结合来处理某些不确定性信息系统可能更为有效。     

An extended grey forecasting model for omnidirectional forecasting considering data gap difference

To achieve effective and efficient decision making in a highly competitive business environment, an enterprise must have an appropriate forecasting technique that can meet the requirements of both timeliness and accuracy. Accordingly, in the early stages, building a forecasting model with incomplete information and limited samples is very important to a business. Grey system theory is one of the prediction methods that can be built with a small sample and yet has a strong ability to make short-term predictions. The purpose of this study is to come up with an improved forecasting model based on the concept of this theory to enlarge the applicability of the grey forecasting model in various situations. By extending the data transforming approach, this method generalizes a building procedure for the grey model to grasp the data outline and information trend. Specifically, a novel inverse accumulating generation operator is developed to enable omnidirectional forecasting. The research utilizes observations of the titanium alloy fatigue limit along with temperature changes as raw data to verify the performance of the proposed method. The experimental results show that not only can this method expand the application scope of the grey forecasting model, but also improve its forecasting accuracy.     

On the properties of small sample of GM(1,1) model

In grey prediction modeling, the more samples selected the more errors. This paper puts forward new explanations of “incomplete information and small sample” of grey systems and expands the suitable range of grey system theory. Based on the geometric sequence, it probes into the influence on the relative errors by selecting the different sample sizes. The research results indicate that to the non-negative increasing monotonous exponential sequence, the more samples selected, the more average relative errors. To the non-negative decreasing monotonous exponential sequence, a proper sample number exists that has the least average relative error. When the initial value of the sequence of raw data of new information GM(1,1) model changes, the development coefficient remains unchanged. The segmental correction new information GM(1,1) model (SNGM) can obviously improve the simulation accuracy. It puts forward the mathematic proofs that the small sample usually has more accuracy than the large sample when establishing GM(1,1) model in theory.     

灰色系统理论中的有界灰矩阵

本文详细地讨论了灰色理论中的有界灰矩阵的运算，给出了若干基本性质，指出有界灰矩阵的定义和运算在形式上是实矩阵相应概念的直接推广，但在运算性质上有极大差异，修正了文献「３」中的几个错误。     

具有分布时滞的灰色随机非线性系统的鲁棒稳定性

利用灰矩阵的矩阵覆盖集的分解技术和矩阵的范数理论及Lyapunov-K rasovsk ii泛函法,研究了具有分布时滞的灰色随机非线性系统的鲁棒稳定性问题,得到了该系统鲁棒稳定的时滞依赖的线性矩阵不等式(LM Is)判据,并通过数值例子说明了所得的"LM Is判据"的有效性和实用性.     

Novel methods on comparing grey numbers

Uncertain decision-making is an important branch of decision-making theory. It is crucial to describe uncertain information, which determine the decision-making is effective or not. This paper first presents a brief survey of the existing methods on denoting uncertain information, such as fuzzy mathematics, stochastic and interval methods, analyzes the merits and demerits of these methods. Then the paper proposes a novel method grey systems theory to describe uncertain information and gives the novel definition of grey number on the basis of probability distribution. Subsequently a novel probability method on comparing grey numbers, especially discrete grey numbers and interval grey numbers, is studied. When an interval grey number satisfied to continuous uniform distribution, it will be degenerated into an interval number. Finally three numerical examples are investigated to demonstrate the effectiveness of the present method.     

第三方物流客户服务绩效的灰色模糊综合评价模型

根据评价第三方物流客户服务绩效的KPI指标体系,考虑到评价过程中信息的不完全性以及评判者思维模糊性的特点,将综合评价中传统的信息集结算子推广到更一般的三角模运算上,以此建立了基于三角模算子的第三方物流客户服务绩效的灰色模糊综合评判模型。作为综合评价中传统信息集结算子的推广,该模型具有更广泛的适用范围。     

基于粒子群算法的变权累加生成的GM(1,1)模型

根据灰色系统的新信息优先原理可知新信息对认知的作用大于旧信息的作用,而传统的累加生成没有体现原始数据中新信息的重要性.针对这一问题引入了变权累加生成的方法,并对变权累加生成在单调性、灰指数规律、凸性等方面的性质进行了研究,得到变权累加生成序列具有单调递增性,具有较强的指数规律,并具有下凸性,这些性质是高精度建模的保证,然后建立了基于变权累加生成的GM(1,1)模型,并运用粒子群算法确定了变权累加生成的权重.通过具体的算例计算表明,变权累加生成的GM(1,1)模型能够提高模型的模拟和预测精度.     

An analysis of the structural complexity of supply chain networks

Each enterprise in a supply chain network needs quantitative indicators to analyze and manage its interactions with different business partners in the network. Supply chains exhibit the characteristics of complex systems. In a supply chain network, a large number of firms cooperate simultaneously with many suppliers and customers, and interact through a variety of information and material flows to achieve a balance between supply and demand. However, the complexity of a supply chain is not a simple linear structure where a small change often results in a chain reaction. When supply chain complexity increases, monitoring and managing the interaction between different elements of the chain becomes more difficult. An entropy model based on information theory provides an appropriate means of quantifying the complexity of a supply chain system by delivering information required to describe the state of the system. The entropy measure links uncertainty and complexity so that, as a system grows in uncertainty, it becomes more complex and more information is required to describe and monitor it. In this paper, we propose an entropy-based measure for analyzing the structural complexity in relation to the structure and system uncertainty. The method provides guidelines for estimating the complexity throughout the supply chain structure.     

基于均衡接近度的不确定型的统计决策方法及其应用

灰熵是离散序列均衡程度的测度,灰色关联度是参考序列和比较序列之间接近程度的测度.该方法将灰色关联度和均衡度合成为均衡接近度,并以此作为决策准则,提出了一种不确定型的统计决策的量化计算方法,从而避免了低层次多因素权重确定的主观性,使决策模型更加合理.通过将该决策方法在华油燃气公司决策中进行应用,为该公司选择了最优决策方案,使其获得了较好的经济效益.     

Application of fuzzy soft set in decision making problems based on grey theory

There are many uncertain problems in practical production and life which need decisions made with soft sets and fuzzy soft sets. However, the basis of evaluation of the decision method is single and simple, the same decision problem can obtain different results from using a different evaluation basis. In this paper, in order to obtain the right result, we discuss fuzzy soft set decision problems. A new algorithm based on grey relational analysis is presented. The evaluation bases of the new algorithm are multiple. There is more information in a decision result based on multiple evaluation bases, which is more easily accepted and logical to one’s thinking. For the two cases examined, the results show that the new algorithm is efficient for solving decision problems.     

Learning Fuzzy Grey Cognitive Maps using Nonlinear Hebbian-based approach

Recently, Fuzzy Grey Cognitive Maps (FGCM) has been proposed as a FCM extension. It is based on Grey System Theory, that it has become a very effective theory for solving problems within environments with high uncertainty, under discrete small and incomplete data sets. The proposed approach of learning FGCMs applies the Nonlinear Hebbian based algorithm determine the success of radiation therapy process estimating the final dose delivered to the target volume. The scope of this research is to explore an alternative decision support method using the main aspects of fuzzy logic and grey systems to cope with the uncertainty inherent in medical domain and physicians uncertainty to describe numerically the influences among concepts in medical domain. The Supervisor-FGCM, trained by NHL algorithm adapted in FGCMs, determines the treatment variables of cancer therapy and the acceptance level of final radiation dose to the target volume. Three clinical case studies were used to test the proposed methodology with meaningful and promising results and prove the efficiency of the NHL algorithm for FGCM approach.     

基于毕达哥拉斯犹豫模糊距离测度的灰关联双向投影决策法

为解决多属性决策单方向偏差难题所提出的灰关联投影法尚存在适用性差的技术不足。为此,在借鉴双向投影技术要点的基础上联合新型灰关联系数,并引入毕达哥拉斯犹豫模糊集的表达优势,提出基于毕达哥拉斯犹豫模糊距离测度的灰关联双向投影决策方法。新方法能够有效解决毕达哥拉斯犹豫模糊多属性决策问题,具有较高的分辨度。通过算例应用分析,直接验证了毕达哥拉斯犹豫模糊灰关联双向投影方法的实用性,间接验证了毕达哥拉斯犹豫模糊距离测度和灰关联双向投影法的有效性。     

多维评价主观判断的有效性研究

本文以灰色系统理论为基础,把专家主观判断的结果表征为“区间灰数”,用灰数统计方法进行统计分析,建立了评价专家主观判断有效性的灰色模型,该模型具有良好的可操作性和有效性．     

大气环境质量预测与评价的灰色定量刻划模型

本文针对大气环境系统中的灰色性，运用灰色系统理论对大气环境质量进行预测与评价．该理论与方法应用于临川市大气环境质量预测与评价中，结果令人满意．     

基于灰色系统的模糊综合评判在产险公司偿付能力评价中的应用

本文将灰色系统中的灰关联分析理论和模糊数学中模糊综合评判法引入保险公司偿付能力的综合评价排序中。通过案例分析，对2005年我国八家主要财产保险公司（以下简称产险公司）的偿付能力作了判断比较，给出了综合排序。     

基于灾变灰预测的电力峰谷差异常的预测模型研究

电力负荷预测过程中,峰谷值波动是一种正常现象,当高峰期负荷急速攀升时,可能出现峰谷差异常问题(如异常值、随机误差项的异方差等),则会给电力企业带来麻烦和损失.为了对负荷峰谷差异常进行有效的预测,构建了一个基于灾变灰预测的电力峰谷差异常的预测模型,并结合实例说明了此模型的可行性和有效性.     

基于系统冲击的灰色预测模型及其应用研究

针对系统受到系统外部冲击问题,结合泛函理论和灰色系统理论,建立了含有系统冲击泛函分析因子的灰色泛函预测模型。并运用贝叶斯网络推理技术,建立了系统冲击与系统控制的灰色贝叶斯网络推理预测模型。所建模型可以分析基于系统冲击演化的泛函分析因子的动态推演问题。依据泛函分析因子的变动,可以预测与修正系统发展趋势。案例分析了2013年房地产经济受到新政策的冲击问题。由于房地产经济受到新政策冲击,使经济发展态势发生转变。根据房地产经济的当前时段信息,利用灰色贝叶斯网络推理预测模型对历史趋势进行修正,预测结果与实际数值仅有3.81%的偏离,预测结果较其它现有模型的预测结果精确。灰色贝叶斯网络推理模型强调对近期数据的开发利用,适用于预测系统近期受到外部冲击的发展趋势问题。