This paper discusses how to deal with the greyness problem in the system from the perspective of “result”. Aiming at the greyness problem of the traditional grey relational analysis result, an information fusion grey relational analysis method based on D-S evidence theory and multi solution information fusion is proposed, which mends the traditional grey relational analysis method. The results show that the method proposed in the study has better effect than the traditional grey relational analysis method, and has higher accuracy in the wear particle identification, which indicates that it can further expand the application scope of the grey relational analysis method.
MaX., XieM. and SuykensJ., A novel neural grey system model with Bayesian regularization and its applications, Neurocomputing456 (2021), 61–75.
2.
LiuS., ZhangH. and YangY., Explanation of terms of grey incidence analysis models, Theory and Application7(1) (2017), 136–142.
3.
YuanC., YangY. and ShuiL.L., Generalized grey relation analysis based on approximate true starting points, Journal of Grey System25 (2013), 71–78.
4.
BalamuruganS., Application of multi-attribute decision making approach for transesterification process using grey relational analysis, Journal of Engineering Science and Technology Review13 (2020), 214–218.
5.
YinM.S., Fifteen years of grey system theory research: A historical review and bibliometric analysis, Expert Systems with Applications40 (2013), 2767–2775.
6.
LiuS., YangY., CaoY., et al, A summary on the research of GRA models, Grey Systems Theory and Application3 (2013), 7–15.
7.
KuoT., A review of some modified grey relational analysis models, Journal of Grey System29 (2017), 70–77.
8.
PanW., JianL. and LiuT., Grey system theory trends from to: A bibliometric analysis and visualization, Scientometrics121 (2019), 1407–1434.
9.
SuvvariA., DuraiR.S.S. and GoyariP., Financial performance assessment using grey relational analysis (GRA) an application to life insurance companies in India, Grey Systems Theory and Application9 (2019), 502–516.
10.
SongX., ZhangW., GeZ., et al., A study of the influencing factors on the carbon emission trading price in China based on the improved gray relational analysis model, , Sustainability14 (2022), 8002.
11.
NurcanE. and KoksalC.D., Determination of financial failure indicators by gray relational analysis and application of data envelopment analysis and logistic regression analysis in bist 100 index, Iranian Journal of Management Studies14 (2021), 163x–163.
12.
XieM., WuL., LiB., et al., A novel hybrid multivariate nonlinear grey model for forecasting the traffic-related emissions, Applied Mathematical Modelling77 (2020), 1242–1254.
13.
NguyenT., LeH.N., NgoV.H., et al., CRITIC method and grey system theory in the study of global electric cars, World Electric Vehicle Journal11 (2020), 1–15.
14.
YangK., LiuS., LiX., et al., D-S evidence theory based trust detection scheme in wireless sensor networks, Journal of Technology & Human Interaction12 (2016), 48–59.
15.
HeH. and PatnaikS., Research on prediction of internet public opinion based on grey system theory and fuzzy neural network, Journal of Intelligent & Fuzzy Systems35 (2018), 325–332.
