1. Introduction
In recent years, there has been an increasing interest in grey relational analysis (GRA) and its applications in decision making. GRA is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management. GRA was first proposed by Deng in 1981. It has since been developed and extended by many researchers. The basic idea of GRA is to construct a grey relational grade (GRG) to represent the degree of similarity between a given data set and a reference set. The reference set is usually constructed by expert knowledge or past experience. The GRG can be used to compare different data sets and to make decisions. For example, in engineering design, GRA can be used to select the best design from a set of alternatives. In medicine, GRA can be used to diagnose diseases. In economics, GRA can be used to forecast market trends.
2. Methodology
2.1. Grey relational grade
The grey relational grade (GRG) is a numerical value that represents the degree of similarity between a given data set and a reference set. It is usually calculated using the following formula: GRG = (1–d/max(d))*100 where d is the Euclidean distance between the given data set and the reference set, and max(d) is the maximum Euclidean distance between any two data points in the reference set.
2.2. Grey relational analysis
Grey relational analysis (GRA) is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management. GRA was first proposed by Deng in 1981. It has since been developed and extended by many researchers. The basic idea of GRA is to construct a grey relational grade (GRG) to represent the degree of similarity between a given data set and a reference set. The reference set is usually constructed by expert knowledge or past experience. The GRG can be used to compare different data sets and to make decisions. For example, in engineering design, GRA can be used to select the best design from a set of alternatives. In medicine, GRA can be used to diagnose diseases. In economics, GRA can be used to forecast market trends.
3. Applications
3.1. Engineering design
GRA has been successfully used in engineering design. For example, it has been used to select the best design from a set of alternatives.
3.2. Medicine
GRA has been successfully used in medicine. For example, it has been used to diagnose diseases.
3.3. Economics
GRA has been successfully used in economics. For example, it has been used to forecast market trends.
4. Conclusion
GRA is a mathematical approach for dealing with imprecision, uncertainty and vagueness in data. It has been successfully used in a variety of fields such as engineering, medicine, economics and management.