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 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.

scholarly journals Research on Evaluation of Core Competence of Listed Liquor Companies Based on Grey Relational Degree

2018 ◽  
Vol 22 (1) ◽  
pp. 25-32
Author(s):  
Weimei Zhang
Keyword(s):  
Technological Innovation ◽  
Core Competence ◽  
Critical Factor ◽  
Innovation Capability ◽  
Research On Evaluation ◽  
The Core ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
The Core Competence ◽  
Grey Relational

Abstract The Grey Relational Degree method was applied to evaluating the core competence of the ten listed liquor companies. The study found that: the most critical factor affecting the strength of the core competence of listed liquor companies is the technological innovation capability; in general, the core competence of the ten listed liquor companies is weak, with an average Grey Relational Degree of 0.752, indicating more potentials for improvement; in addition, with the exception of Kweichou Moutai, the other nine listed liquor companies are all in an uncoordinated state regarding the development of the six capabilities, and they usually have one or several capabilities lagged behind.

Development of a direct NGM(1,1) prediction model based on interval grey numbers

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ye Li ◽  
Yuanping Ding ◽  
Yaqian Jing ◽  
Sandang Guo
Keyword(s):  
Genetic Algorithm ◽  
Markov Model ◽  
Prediction Model ◽  
Real Number ◽  
Content Type ◽  
Optimal Weight ◽  
Practical Applications ◽  
Number Sequences ◽  
Integral Development ◽  
Grey Number

PurposeThe purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.Design/methodology/approachA definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.FindingsThe interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.Practical implicationsUncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.Originality/valueThe main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

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.

Grey relational decision making model of three-parameter interval grey number based on AHP and DEA

2019 ◽  
Vol 10 (1) ◽  
pp. 25-37
Author(s):  
Bingjun Li ◽  
Xiaoxiao Zhu
Keyword(s):  
Decision Making ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Agriculture Industry ◽  
Parameter Interval ◽  
Grey Number ◽  
Index Weight ◽  
Decision Making Model ◽  
Grey Relational ◽  
Grey Relation

Purpose The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers. Design/methodology/approach First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes. Findings The effectiveness of the model is proved by an example of carrier aircraft selection. Practical implications The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields. Originality/value In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.

The Grey Relational Analysis of Driving Forces for Water Consumed Structure

2012 ◽  
Vol 490-495 ◽  
pp. 1612-1616 ◽  
Author(s):  
Cui Mei Lv ◽  
Fa Xing Du
Keyword(s):  
Grey Relational Analysis ◽  
Driving Forces ◽  
Water Saving ◽  
Utilization Rate ◽  
Sustainable Utilization ◽  
Urbanization Level ◽  
Relational Analysis ◽  
Grey Relational Degree ◽  
Relational Degree ◽  
Grey Relational

Grey Relational Analysis is a method of analysis and calculates the relational degree of evaluated object, which can characterize the relational degree between object with viral object. In this paper it was used to analyze the driving forces of water consumed structure change, and YiChang city was selected as an example. Adopted grey relational degree analysis, the main factors were found out. The results showed that industry water utilization rate, irrigation area, urbanization level are the main driving forces, and corresponding water-saving measures were put forward. This study can provide reference for the construction of water-saving society and sustainable utilization of water resource.

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