Journal of Systems Engineering and Electronics ›› 2014, Vol. 25 ›› Issue (5): 840-847.doi: 10.1109/JSEE.2014.00097

• SYSTEMS ENGINEERING • Previous Articles     Next Articles

Comprehensive multivariate grey incidence degree based on principal component analysis

Ke Zhang*, Yintao Zhang, and Pinpin Qu   

  1. Business School, Hohai University, Nanjing 211100, China
  • Online:2014-10-23 Published:2010-01-03


To overcome the too fine-grained granularity problem of multivariate grey incidence analysis and to explore the comprehensive incidence analysis model, three multivariate grey incidences degree models based on principal component analysis (PCA) are proposed. Firstly, the PCA method is introduced to extract the feature sequences of a behavioral matrix. Then, the grey incidence analysis between two behavioral matrices is transformed into the similarity and nearness measure between their feature sequences. Based on the classic grey incidence analysis theory, absolute and relative incidence degree models for feature sequences are constructed, and a comprehensive grey incidence model is proposed. Furthermore, the properties of models are researched. It proves that the proposed models satisfy the properties of translation invariance, multiple transformation invariance, and axioms of the grey incidence analysis, respectively. Finally, a case is studied. The results illustrate that the model is effective than other multivariate grey incidence analysis models.