A novel grey fixed weight cluster model based on interval grey numbers

2017 ◽  
Vol 7 (2) ◽  
pp. 156-167 ◽  
Author(s):  
Jing Ye ◽  
Yaoguo Dang
Keyword(s):  
Weight Function ◽  
Cluster Model ◽  
Medical Science ◽  
Grey System Theory ◽  
Central Point ◽  
Content Type ◽  
Model Based ◽  
Clustering Model ◽  
Fixed Weight ◽  
Grey Clustering

Purpose Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers. Design/methodology/approach A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained. Findings The model is verified by a case which reveals a high distinguishability, validity and practicability. Practical implications This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on. Originality/value The central-point triangular whitenization weight function is introduced. The method reflects the thought “make full use of the information” in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.

Evaluation of auto parts remanufacturing by grey cluster model

2016 ◽  
Vol 6 (3) ◽  
pp. 296-308 ◽  
Author(s):  
Jianghui Xin
Keyword(s):  
Index System ◽  
Cluster Model ◽  
Coefficient Matrix ◽  
Clustering Coefficient ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Content Type ◽  
Fixed Weight ◽  
Grey Clustering ◽  
Auto Parts

Purpose With the improvement of economic level, car ownership is growing, and the number of scrapped automobiles is increasing. Therefore, evaluation research for auto parts remanufacturing is particularly important. The purpose of this paper is to construct the evaluation index system of auto parts remanufacturing and research the grey clustering theory. The grey fixed weight clustering evaluation is used to evaluate automobile engine remanufacturability. Design/methodology/approach According to the policies and regulations of China about remanufacturing, economic, technical, resources, energy and the environment, four indexes are selected to set up the evaluation standard of auto parts remanufacturing scheme. Grey fixed weight clustering method is used to evaluate remanufacturability of the auto parts. Firstly, number index and grey determine the whitenization weight function, then based on the clustering weight of each index, the clustering coefficient matrix is calculated. Finally, the class that certain object belongs to, according to the clustering coefficient matrix is determined. Findings Results show that constructed indexes of auto parts remanufacturing scheme can be used for effective evaluation. And the proposed fixed weight grey cluster model can aggregate all indexes information well. Therefore, the proposed indexes and model in this paper are effective and can be used for auto parts remanufacturing. Practical implications According to the requirements of the current situation in China, this paper puts forward a method based on grey clustering decision, to evaluate different auto parts remanufacturing schemes, for manufacturing enterprises to provide theoretical basis for remanufacturing production, in order to realize the reasonable configuration of resources. Originality/value This paper firstly establishes the evaluation index system of auto parts remanufacturing, the grey clustering theory is introduced into the evaluation of remanufacturing. The fixed-weight grey cluster model is proposed to aggregate indexes’ information.

Grey cluster evaluation models based on mixed triangular whitenization weight functions

2015 ◽  
Vol 5 (3) ◽  
pp. 410-418 ◽  
Author(s):  
Si-feng Liu ◽  
Yingjie Yang ◽  
Zhi-geng Fang ◽  
Naiming Xie
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Weight Functions ◽  
The Novel ◽  
Content Type ◽  
Clustering Model ◽  
Cluster Evaluation ◽  
Fixed Weight ◽  
Variable Weight ◽  
Evaluation Models

Purpose – The purpose of this paper is to present two novel grey cluster evaluation models to solve the difficulty in extending the bounds of each clustering index of grey cluster evaluation models. Design/methodology/approach – In this paper, the triangular whitenization weight function corresponding to class 1 is changed to a whitenization weight function of its lower measures, and the triangular whitenization weight function corresponding to class s is changed to a whitenization weight function of its upper measures. The difficulty in extending the bound of each clustering indicator is solved with this improvement. Findings – The findings of this paper are the novel grey cluster evaluation models based on mixed centre-point triangular whitenization weight functions and the novel grey cluster evaluation models based on mixed end-point triangular whitenization weight functions. Practical implications – A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function. Originality/value – Particularly, compared with grey variable weight clustering model and grey fixed weight clustering model, the grey cluster evaluation model using whitenization weight function is more suitable to be used to solve the problem of poor information clustering evaluation. The grey cluster evaluation model using endpoint triangular whitenization weight functions is suitable for the situation that all grey boundary is clear, but the most likely points belonging to each grey class are unknown; the grey cluster evaluation model using centre-point triangular whitenization weight functions is suitable for those problems where it is easier to judge the most likely points belonging to each grey class, but the grey boundary is not clear.

Quantitative analysis of the cities’ innovation capacity based on grey variable weight clustering

2015 ◽  
Vol 5 (3) ◽  
pp. 344-353
Author(s):  
Yeqing Guan ◽  
Hua Liu ◽  
Ying Zhu
Keyword(s):  
Quantitative Analysis ◽  
Weight Function ◽  
Design Methodology ◽  
Clustering Method ◽  
Content Type ◽  
Clustering Model ◽  
Variable Weight ◽  
Grey Clustering ◽  
Innovation Capacity

Purpose – The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found. Design/methodology/approach – First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps. Findings – The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering. Originality/value – This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

Grey clustering evaluation based on AHP and interval grey number

2019 ◽  
Vol 12 (1) ◽  
pp. 127-137 ◽  
Author(s):  
Kejia Chen ◽  
Ping Chen ◽  
Lixi Yang ◽  
Lian Jin
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Evaluation Indexes ◽  
Clustering Model ◽  
Grey Clustering ◽  
Evaluation Problem ◽  
Grey Number ◽  
Mean Function

PurposeThe 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/approachFirst, 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.FindingsWhen 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/valueThe 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.

Grey clustering model based on kernel and information field

2019 ◽  
Vol 10 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Dang Luo ◽  
Zhang Huihui
Keyword(s):  
Weight Function ◽  
Fuzzy Clustering ◽  
Theoretical Basis ◽  
Disaster Risk ◽  
Content Type ◽  
Clustering Model ◽  
Grey Clustering ◽  
Drought Disaster ◽  
Regional Drought ◽  
Grey Number

Purpose The purpose of this paper is to propose a grey clustering model based on kernel and information field to deal with the situation in which both the observation values and the turning points of the whitenization weight function are interval grey numbers. Design/methodology/approach First, the “unreduced axiom of degree of greyness” was expanded to obtain the inference of “information field not-reducing”. Then, based on the theoretical basis of inference, the expression of whitenization weight function with interval grey number was provided. The grey clustering model and fuzzy clustering model were compared to analyse the relationship and difference between the two models. Finally, the paper model and the fuzzy clustering model were applied to the example analysis, and the interval grey number clustering model was established to analyse the influencing factors of regional drought disaster risk in Henan Province. Findings The example analysis results illustrate that although the two clustering methods have different theoretical basis, they are suitable for dealing with complex systems with uncertainty or grey characteristic, solving the problem of incomplete system information, which has certain feasibility and rationality. The clustering results of case study show that five influencing factors of regional drought disaster risk in Henan Province are divided into three classes, consistent with the actual situation, and they show the validity and practicability of the clustering model. Originality/value The paper proposes a new whitenization weight function with interval grey number that can transform interval grey number operations into real number operations. It not only simplifies the calculation steps, but it has a great significance for the “small data sets and poor information” grey system and has a universal applicability.

Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application

2020 ◽  
Vol 10 (4) ◽  
pp. 413-423
Author(s):  
Honghua Wu ◽  
Zhongfeng Qu
Keyword(s):  
Panel Data ◽  
Design Methodology ◽  
Weighted Function ◽  
Content Type ◽  
New Model ◽  
Model Based ◽  
Clustering Model ◽  
Analysis Theory ◽  
Incidence Analysis

PurposeThe paper aims to propose a clustering model for panel data. More specifically, the paper aims to construct a gray incidence model for panel data to solve the classification with multi-factors and multi-attributes.Design/methodology/approachThe paper opted for a clustering theory study using gray incidence theory based on dynamic weighted function. The paper presents an example to verify the rationality of the new model, which suggests that the new model can reflect the incidence degree of panel data.FindingsThe paper provides a new gray incidence model based on a dynamic weighted function that can amplify the characteristics of the sample to some extent. The properties of the new incidence model, such as normalization, symmetry and nearness, are all satisfied. The paper also shows that the new incidence model performs very well on cluster discrimination.Originality/valueThe new model in this paper has supplemented and improved the gray incidence analysis theory for panel data.

scholarly journals Explanation of terms of grey clustering evaluation models

2017 ◽  
Vol 7 (1) ◽  
pp. 129-135 ◽  
Author(s):  
Sifeng Liu ◽  
Yingjie Yang
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
Grey Clustering ◽  
Evaluation Models ◽  
Key Terms ◽  
Practical Implications

Purpose The purpose of this paper is to present the terms of grey clustering evaluation models. Design/methodology/approach The definitions of basic terms about grey clustering evaluation models are presented one by one. Findings The reader could know the basic explanation about the important terms about various grey clustering evaluation models from this paper. Practical implications Many of the authors’ colleagues thought that unified definitions of key terms would be beneficial for both the readers and the authors. Originality/value It is a fundamental work to standardise all the definitions of terms for a new discipline. It is also propitious to spread and universal of grey system theory.

Research on safety evaluation of civil aircraft based on the grey clustering model

2018 ◽  
Vol 8 (1) ◽  
pp. 110-120 ◽  
Author(s):  
Bentao Su ◽  
Naiming Xie
Keyword(s):  
Safety Assessment ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Safety Level ◽  
Content Type ◽  
Aircraft Manufacturing ◽  
Clustering Model ◽  
Grey Clustering ◽  
Civil Aircraft

Purpose The purpose of this paper is to construct a grey clustering model based on the nonlinear whitenization weight function and to assess the safety of civil aircraft by using a quantitative method. Design/methodology/approach According to the running stage of civil aircraft safety assessment issues, first the civil aircraft safety evaluation index system is constructed by using a qualitative method. Taking the information duplication between indicators, the grey relational analysis method is used to filter the key indicators, then the grey clustering evaluation model of nonlinear whitening right function is built to evaluate the safety of civil aircraft and the algorithm steps of the evaluation model are given. Finally, the model is validated by collecting the parameters of nine different civil aircrafts at home and abroad. Findings The results show that the safety level of different types of aircraft is different due to the different index parameters, and to some extent, explain the rationality and scientificity of the method proposed in this paper to solve the problem. Practical implications This paper gives a complete set of security assessment methods, which can be used to evaluate the security of civil aircraft in the operational phase quantitatively, scientifically and reasonably. Furthermore, it can be extended to other complex system security or stability assessment issues. Originality/value It not only provides the supplement and perfection of the safety assessment method in the theoretical system to a certain extent, but also provides a theoretical guidance to solve the problem of civil aircraft system safety assessment of civil aircraft manufacturing enterprise all over the world. At the same time, the nonlinear grey clustering evaluation model constructed in this paper is an improvement of the traditional model, which is, to some extent, the improvement of the grey clustering evaluation theory.

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