Systems Engineering and Electronics ›› 2018, Vol. 40 ›› Issue (3): 509-517.doi: 10.3969/j.issn.1001-506X.2018.03.04

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Multi-source heterogeneous data fusion recognition based on statistical correlation coefficients between hesitant fuzzy sets

GUAN Xin, SUN Guidong, YI Xiao, ZHAO Jing   

  1. Department of Electronics and Information Engineering, Naval Aviation University, Yantai 264001, China
  • Online:2018-02-26 Published:2018-02-24

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Abstract:

In order to solve the multi-source heterogeneous data fusion recognition problem in which the multi-source heterogeneous data is expressed as hesitant fuzzy element (HFE), intuitionistic fuzzy number (IFN), interval number and real number, a correlation coefficient between hesitant fuzzy sets (HFSs) is proposed to recognize these data in the HFS domain. Firstly, the multisource heterogeneous data is transformed into the HFS domain. After towards, pointing out the weakness of the existing correlation coefficients between HFS, we propose a correlation coefficient between two HFSs which has advantages in three aspects: firstly it conforms to the statistics intuition, secondly, it is not necessary to have the same length in membership, and thirdly， it is more theoretical in mathematics. Finally, apply the proposed correlation coefficient to recognize the multi-source heterogeneous data based on the principle of the maximum correlation coefficient. The simulation examples demonstrate the vadidity of the proposed correlation coefficient with a comparision analysis, and prove its advantages in high precision and discrinination.

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