scholarly journals Assessment of Agricultural Performance of Districts of Punjab Based On Composite Agricultural Indicators Using Grey Relational Analysis

2021 ◽  
Vol VI (I) ◽  
pp. 158-172
Author(s):  
Tehmina Fiaz Qazi ◽  
Abdul Aziz Khan Niazi ◽  
Abdul Basit Basit
Keyword(s):  
System Theory ◽  
Agricultural Development ◽  
Grey System Theory ◽  
Grey System ◽  
Cross Sectional ◽  
Relational Analysis ◽  
Agricultural Performance ◽  
Development Policies ◽  
Grey Relational Grade ◽  
Grey Relational

The province of Punjab is considered the breadbasket of Pakistan. This study is aimed to evaluate and hierarchicalize the districts of Punjab based on agricultural indicators. It follows a ranking approach that uses secondary cross-sectional data obtained from Punjab Development Statistics 2016. This study has employed Grey System Theory and used GRA. It is a seminal study that uses a unique methodology that has integrated thirteen different indicators of agricultural development in one mathematical model and assigned a distinct composite grade to every district. Findings revealed that district Bahawalpur and Bahawalnagar have the highest Grey Relational Grade (GRG), hence depict the best agricultural performance in Punjab, whereas district Mianwali has the lowest GRG and accordingly least performance. This research provides insight to the policymakers, which will help them to take corrective measures and/or adjust the agricultural development policies

The proof of a new modified grey relational grade

2016 ◽  
Vol 6 (2) ◽  
pp. 180-186 ◽  
Author(s):  
Kunli Wen
Keyword(s):  
System Theory ◽  
Grey Relational Analysis ◽  
Design Methodology ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
The Past ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

Purpose – Until now, many different varieties of grey relational grade methods had been proposed, and there are also many relevant publications. However, in one article published in 2007, which applied the previous grey relational grade to environmental protection fields and some results had been found. After studied it carefully, the author found that the grey relational grade in the paper was not the previous grey relational grade. According to the mathematics logic, it must first prove the proposed grey relational grade satisfies the four axioms in grey relational analysis, and then the author can say that the achieved results are reasonable and correct. The paper aims to discuss these issues. Design/methodology/approach – The paper lists the rational and regular grey relational grade that had been published in the past, and used the four axioms in grey system theory to prove the Pai’s grey relational grade that satisfy the four axioms steps by steps. Findings – Through the detail proof of the proposed grey relational grade in Pai’s paper, it indeed satisfies the four axioms in grey relational grade. Research limitations/implications – The paper had enhanced the correctness and reasonableness of that paper, and let the grey relational grade, which appear in Pai’s paper is legitimate and correct grey relational grade in grey system theory. Originality/value – The paper had identified that Pai’s grey relational grade is a rational and regular grey relational grade in grey system theory, and it proves that the results in Pai’s paper are correct and reasonable.

Study on the second grey relational grade and its properties

Kybernetes ◽  
2010 ◽  
Vol 39 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Yan Ma
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Grey System ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Practical Implications

PurposeThe purpose of this paper is to propose a second relational grade based on the general grey relational grade and analyze several of its properties.Design/methodology/approachGrey system theory. The paper proposes and studies second grey relational grade, establishes second grey relational formula, and studies several characteristics of second grey relational formula.FindingsProposing a second relational grade proved it could solve the problem of the parallelism partly and weaken relativity of space position.Research limitations/implicationsUntil now, the problem of the consistency could not be solved, nor could the problem of the effect which keeps the sequence the same.Practical implicationsThe precision of the grey forecasting model could be strengthened if used in the forecasting model.Originality/valueThe general relational grade only thinks over the relation between two sequences but does not involve the relation in one sequence. The second relational grade considers these two, so if the forecasting model is established with it, the model should be more exact.

Applying grey system theory to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries

2016 ◽  
Vol 6 (2) ◽  
pp. 143-168 ◽  
Author(s):  
Wuwei Li
Keyword(s):  
System Theory ◽  
Grey System Theory ◽  
High Tech ◽  
Grey System ◽  
Content Type ◽  
Relational Analysis ◽  
Panel Data Regression ◽  
Data Regression ◽  
Grey Relational ◽  
The Relationship

Purpose – For the studies whose purposes are to evaluate the relationship between industrial characteristics and innovation activities of the enterprises, there are some limitations in the measures of industrial characteristics and using traditional statistical techniques. The purpose of this paper is to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries using grey system theory. The research results show that grey system theory is suitable to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries. Design/methodology/approach – This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises. First, based on the data on Chinese large and medium-sized high-tech enterprises for the period of 2011-2013, this paper applies grey relational analysis to identify the relatively most important indexes on affecting innovation capabilities of Chinese high-tech enterprises. Second, based on the results from grey relational analysis, this study draws a ranking of the five Chinese high-tech industries in terms of innovation capabilities by grey decision making. Finally, based on the results from grey decision making, this study applies GM (0, N) model to investigate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries. Findings – The results of this study show that in the evaluation indexes system of innovation capabilities of high-tech enterprises, personnel in R & D institutions, R & D personnel, internal expenditure on R & D, expenditure on new product development, expenditure on technology imports, expenditure on technology renovation, and expenditure on technology assimilation and absorption are relatively most important elements affecting innovation capabilities of Chinese high-tech enterprises. In addition, the two top ranking on innovation capabilities are manufacture of electronic equipment and communication equipment, and manufacture of medicines. At last, the findings indicate that in the measures of industrial characteristics, the three top ranking on affecting innovation capabilities of Chinese high-tech enterprises are R & D intensity, technology absorption intensity of indigenous high-tech enterprises and foreign-invested enterprises size. The opening level is in the middle position. Technology intensity, market concentration, and state-owned enterprises size are the three bottom ranking on affecting innovation capabilities of Chinese high-tech enterprises. Research limitations/implications – This study has some limitations. First, this study is limited to Chinese high-tech industries. The findings may not be applicable to other countries’ high-tech industries. Further studies with other countries’ high-tech industries could be extended and examined how industrial characteristics affect innovation capabilities of the firms in these industries. Second, the measures of industrial characteristics proposed in this study are somewhat theoretically weak. In the future, the authors will further improve the current analysis, and develop the measures of industrial characteristics. Finally, with the advent of the more data with the consistent statistical coverage released by China’s National Bureau of Statistics during the more continuous years, other methods, such as panel data regression model in econometrics could be used to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries. By then, the scholars can compare the results from grey system theory and those from panel data regression model in econometrics. Practical implications – Appropriate industrial environment is favorable for Chinese high-tech enterprises to feed their innovation capabilities. Scientific evaluation on the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries is of great significance for Chinese high-tech enterprises in exerting technological catch-up and promoting their competitive advantage. The purposed measures of industrial characteristics and innovation capabilities of high-tech enterprises in this paper, and combined methodology based on grey system theory could be applied to evaluate the relationship between industrial characteristics and innovation capabilities of Chinese high-tech enterprises. Originality/value – This paper proposes the measures of industrial characteristics and innovation capabilities of high-tech enterprises, and uses grey system theory to evaluate the relationship between industrial characteristics and innovation capabilities within Chinese high-tech industries.

The most livable city selection in Turkey with the grey relational analysis

2020 ◽  
Vol 10 (4) ◽  
pp. 529-544 ◽  
Author(s):  
Erkan Kose ◽  
Danışment Vural ◽  
Gulcin Canbulut
Keyword(s):  
System Theory ◽  
Grey Relational Analysis ◽  
Complex Problem ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
Relational Analysis ◽  
Decision Points ◽  
Criteria Weights ◽  
Grey Relational

PurposeThis study has two main objectives: (1) to expand the application areas of grey system theory and (2) to select the most livable city in Turkey.Design/methodology/approachChoosing the most livable city is a complex problem that requires many criteria to be considered. It is important to select decision points according to which the criteria selection will be made and to what extent the criteria will affect the evaluation. For this purpose, a questionnaire was prepared to determine the criteria to be used in the assessment. The survey results were evaluated by the factor analysis (FA) and it was found that the criteria included in the survey were grouped under seven factors. Then, criteria weights were assigned to the determined criteria using the analytic hierarchy process (AHP). At the last stage, Turkey's six most popular cities are graded using the grey relational analysis (GRA) to reduce the uncertainty existing in the process of evaluation.FindingsThe obtained results indicated that the most livable city in Turkey is Istanbul. Istanbul is followed by Izmir, Antalya, Eskisehir, Bursa and Ankara, respectively. Considering that Istanbul is a center of attraction in many respects, this result is not a surprise for many people. It is also observed that the results obtained overlap with similar studies in the literature.Originality/valueGrey system theory and grey numbers have not been previously used to select the most livable city. With this aspect, this study has expanded the application of grey system theory and made an important contribution to the literature.

An Algorithm of the Misconception Order

2013 ◽  
Vol 284-287 ◽  
pp. 3010-3014 ◽  
Author(s):  
Tian Wei Sheu ◽  
Ching Pin Tsai ◽  
Jian Wei Tzeng ◽  
Tzu Liang Chen ◽  
Masatake Nagai
Keyword(s):  
System Theory ◽  
Grey Relational Analysis ◽  
Academic Community ◽  
Grey System Theory ◽  
Remedial Instruction ◽  
Grey System ◽  
Relational Analysis ◽  
The Right ◽  
Grey Relational ◽  
Student Problem

In recent years, the grey system theory is widely applied in academic community, and it has obtained very good results. This study proposes GSP chart (Grey Student-Problem chart) which combines S-P chart (Student-Problem chart) with GRA (Grey Relational Analysis). This paper selects the students whose Gamma value is near 0.5 from the GSP chart. Those students are defined as the Misconception-Students. Then we analyze those students’ answers and select the right and wrong answers created by those Misconception Students at the different time. It is said that the misconception questions are defined as the Misconception-Problem Chart. Through the Problem-Concept Chart conducted by teachers, we can define the Misconception Rate and the Misconception Order by comparing the Misconception-Concept Chart. This method can not only accurately predict where students’ misconception is, but also apply to the remedial instruction.

scholarly journals Grey Relational Grade Based Quantitative Analysis of the Factors Influencing the Load Characteristics of A Power Grid

2018 ◽  
Vol 53 ◽  
pp. 01012 ◽  
Author(s):  
Wei Pan ◽  
Caijia Lei ◽  
Wei Jia ◽  
Hui Gao ◽  
Binghua Fang
Keyword(s):  
Quantitative Analysis ◽  
Influencing Factors ◽  
Power Grid ◽  
Grey System Theory ◽  
Grey System ◽  
Factors Affecting ◽  
Grey Relational Grade ◽  
Load Characteristics ◽  
Grey Relational ◽  
Main Factor

Regarding analysis of load characteristics of a power grid, there are multiple factors that influence the variation of load characteristics. Among these factors, the influence of different ones on the change of load characteristic is somewhat different, thus the degree of influence of various factors needs to be quantified to distinguish the main and minor factors of load characteristics. Based on this, the grey relational analysis in the grey system theory is employed as the basis of mathematical model in this paper. Firstly, the main factors affecting the load characteristics of a power grid are analysed. Then, the principle of quantitative analysis of the influencing factors by using grey relational grade is introduced. Lastly, the load of Guangzhou power grid is selected as the research object, thereby the main factor of temperature affecting the load characteristics is quantitatively analysed, such that the correlation between temperature and load is established. In this paper, by investigating the influencing factors and the degree of influence of load characteristics, the law of load characteristics changes can be effectively revealed, which is of great significance for power system planning and dispatching operation.

scholarly journals Grey Relational Analysis (GRA) as an Effective Method of Research into Social Preferences in Urban Space Planning

Land ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 102
Author(s):  
Małgorzata Gerus-Gościewska ◽  
Dariusz Gościewski
Keyword(s):  
Urban Space ◽  
Social Preferences ◽  
Planning Process ◽  
Grey System Theory ◽  
Grey System ◽  
Space Planning ◽  
Relational Analysis ◽  
Analysis Process ◽  
Survey Results ◽  
Grey Relational

The appearance of urban space is most often determined by planners, urbanists, and officials who fail to consider social preferences in the planning process. According to recent scientific research, spatial design should take into account people’s preferences with regard to its shape, as it is they who are the target audience. Moreover, legal regulations in many countries require the public’s inclusion into the space planning process. This paper outlines the legal status of the issue of social participation in spatial planning and provides an overview of the methods and techniques applied in the research into preferences. The aim of the article is to determine the strength of the relationship between the features adopted for the study using the grey system theory and to investigate the model’s behaviour for varied input data. It also presents the results of a study into the effect of geospatial features on the perception of the sense of security within urban space. The features were extracted using a heuristic method for solving research problems (i.e., brainstorming) and the survey was conducted by the point-scoring method. The survey results were processed by the grey system method according to the grey system theory (GST) of the grey relational analysis (GRA) type to yield a sequence of the strength of dependence between the analysed features. The study was conducted five times, with the order of entering the survey results being changed. The conducted analyses indicated that a change in the order of data from particular surveys applied for calculations resulted in the order of the epsilon coefficients in the significance sequences being changed. The analysis process was modified in order to obtain a stable significance sequence irrespective of the order of entering survey results in the analysis process. The analysis results in the form of a geospatial feature significance sequence provide information as to which of them have the greatest impact on the phenomenon under consideration. The research method can be applied to solve practical problems related to social participation.

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