A new grey comprehensive relational model based on weighted mean distance and induced intensity and its application

2019 ◽  
Vol 9 (3) ◽  
pp. 374-384 ◽  
Author(s):  
Kedong Yin ◽  
Jie Xu ◽  
Xuemei Li
Keyword(s):  
Water Resources ◽  
Relational Model ◽  
Weighted Mean ◽  
Domestic Water ◽  
Content Type ◽  
Correlation Degree ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational ◽  
Degree Of Similarity

Purpose The purpose of this paper is to study the essential characteristics of grey relational degree of proximity, to analyse the abstract meaning of grey relational degree of similarity and fully consider the two different relational degree models. Design/methodology/approach The paper constructed the grey proximity relational degree by using the weighted mean distance. To analyse the motivation of the development of things, this paper constructed the grey similarity degree by using the concept of induced strength. Finally, the two correlation models are weighted by reliability weighting. Findings The research finding shows that the distance is the essence of the grey relational degree of proximity, and the induced strength is a good explanation of the similarities in the development of things. Practical implications The analyses imply that the total amount of water consumption in China has the greatest correlation with the consumption of agricultural water resources, followed by the consumption of industrial water resources, and the least correlation with the consumption of domestic water resources. Originality/value The paper succeeds in realizing the essential characteristics of grey relational degree of proximity and the abstract meaning of grey relational degree of similarity. Besides, the resolution of the correlation degree can be greatly improved by reliability weighting.

Analysis on the related factors of China's technological innovation ability using greyness relational degree

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Li Li ◽  
Xican Li
Keyword(s):  
Real Number ◽  
Technological Innovation ◽  
Number Sequence ◽  
Related Factors ◽  
Content Type ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Innovation Ability ◽  
Grey Number ◽  
Grey Relational

PurposeIn order to make grey relational analysis applicable to the interval grey number, this paper discusses the model of grey relational degree of the interval grey number and uses it to analyze the related factors of China's technological innovation ability.Design/methodology/approachFirst, this paper gives the definitions of the lower bound domain, the value domain, the upper bound domain of interval grey number and the generalized measure and the generalized greyness of interval grey number. Then, based on the grey relational theory, this paper proposes the model of greyness relational degree of the interval grey number and analyzes its relationship with the classical grey relational degree. Finally, the model of greyness relational degree is applied to analyze the related factors of China's technological innovation ability.FindingsThe results show that the model of greyness relational degree has strict theoretical basis, convenient calculation and easy programming and can be applied to the grey number sequence, real number sequence and grey number and real number coexisting sequence. The relational order of the four related factors of China's technological innovation ability is research and development (R&D) expenditure, R&D personnel, university student number and public library number, and it is in line with the reality.Practical implicationsThe results show that the sequence values of greyness relational degree have large discreteness, and it is feasible and effective to analyze the related factors of China's technological innovation ability.Originality/valueThe paper succeeds in realizing both the model of greyness relational degree of interval grey number with unvalued information distribution and the order of related factors of China's technological innovation ability.

A study of the grey relational model of interval numbers for panel data

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kedong Yin ◽  
Tongtong Xu ◽  
Xuemei Li ◽  
Yun Cao
Keyword(s):  
Panel Data ◽  
Treatment Method ◽  
Interval Number ◽  
Two Dimensions ◽  
Relational Model ◽  
Interval Numbers ◽  
Cross Sectional ◽  
Content Type ◽  
Correlation Degree ◽  
Grey Relational

PurposeThis paper aims to deal with the grey relational problem of panel data with an attribute value of interval numbers. The grey relational model of interval number for panel data is constructed in this paper.Design/methodology/approachFirst, three kinds of interval grey relational operators for the behavior sequence of a dimensionless system are proposed. At the same time, the positive treatment method of interval numbers for cost-type and moderate-type indicators is put forward. On this basis, the correlation between the three-dimensional interval numbers of panel data is converted into the correlation between the two-dimensional interval numbers in time series and cross-sectional dimensions. The grey correlation coefficients of each scheme and the ideal scheme matrix are calculated in the two dimensions, respectively. Finally, the correlation degree of panel interval number and scheme ordering are obtained by arithmetic mean.FindingsThis paper proves that the grey relational model of the panel interval number still has the properties of normalization, uniqueness and proximity. It also avoids the problem that the results are not unique due to the different orders of objects in the panel data.Practical implicationsThe effectiveness and practicability of the model is verified by taking supplier selection as an example. In fact, this model can also be widely used in agriculture, industry, society and other fields.Originality/valueThe accuracy of the relational results is higher and more accurate compared with the previous studies.

scholarly journals Relational Research between China’s Marine S&T and Economy Based on RPGRA Model

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Kedong Yin ◽  
Suyuan Li ◽  
Xuemei Li
Keyword(s):  
Time Series ◽  
Relational Model ◽  
The Poor ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Marine Economy ◽  
Change Trend ◽  
Grey Relational ◽  
Relative Change ◽  
The Relationship

To make up the defect of the existing model, an improved grey relational model based on radian perspective (RPGRA) is put forward. According to the similarity of the relative change trend of time series translating traditional grey relational degree into radian algorithm within different piecewise functions, it greatly improves the accuracy and validity of the research results by making full use of the poor information in time series. Meanwhile, the properties of the RPGRA were discussed. The relationship between China’s marine S&T and marine economy is researched using the new model, so the validity and creditability of RPGRA are illustrated. The empirical results show that marine scientific and technological research projects, marine scientific and technological patents granted, and research funds receipts of the marine scientific research institutions have greater relationship with GOP, which indicates that they have more impact on China’s marine economy.

Shot Performance Influence Factors Correlation Degree Analysis Based on Genetic Algorithms

2015 ◽  
Vol 9 (1) ◽  
pp. 479-482
Author(s):  
Lei Yang
Keyword(s):  
Genetic Algorithms ◽  
Scientific Evidence ◽  
Influence Factors ◽  
Scientific Training ◽  
Physical Knowledge ◽  
Five Factors ◽  
Correlation Degree ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

The paper first applies Newton mechanical and physical knowledge to establish shot throwing process mathematical model, calculates throwing performance is related to α, h, t0, v0 these five factors; the next, it adopts genetic algorithms to solve optimal throwing performance and these five factors best parameters, from which optimal throwing performance is 21.78m; finally it adopts grey relational degree algorithms and analyzes five influence factorsf primary and secondary relation is α, h, t0, v0 , which provides scientific evidence for making scientific training plans, during training, coaches and athletes should pay attention to foster strengths and circumvent weakness, give their own advantages into full play so that can get more ideal results.

Air Conditioning Load Prediction of an Office Building based on Long Short Term Memory Neural Network

2021 ◽  
Vol 14 ◽  
Author(s):  
Mengxiang Zhuang ◽  
Qixin Zhu
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Load Prediction ◽  
Short Term ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Long Short Term Memory ◽  
Grey Relational

Background: Energy conservation has always been a major issue in our country, and the air conditioning energy consumption of buildings accounts for the majority of the energy consumption of buildings. If the building load can be predicted and the air conditioning equipment can respond in advance, it can not only save energy, but also extend the life of the equipment. Introduction: The Neural network proposed in this paper can deeply analyze the load characteristics through three gate structures, which is helpful to improve the prediction accuracy. Combined with grey relational degree method, the prediction speed can be accelerated. Method: This paper introduces a grey relational degree method to analyze the factors related to air conditioning load and selects the best ones. A Long Short Term Memory Neural Network (LSTMNN) prediction model was established. In this paper, grey relational analysis and LSTMNN are combined to predict the air conditioning load of an office building, and the predicted results are compared with the real values. Results: Compared with Back Propagation Neural Network (BPNN) prediction model and Support Vector Machine (SVM) prediction model, the simulation results show that this method has better effect on air conditioning load prediction. Conclusion: Grey relational degree analysis can extract the main factors from the numerous data, which is more convenient and quicker without repeated trial and error. LSTMNN prediction model not only considers the relation of air conditioning load on time series, but also considers the nonlinear relation between load and other factors. This model has higher prediction accuracy, shorter prediction time and great application potential.

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