RAPID SELECTING UAVs FOR COMBAT BASED ON THREE-WAY MULTIPLE ATTRIBUTE DECISION, 1-8.

Yuehao Yan,∗,∗∗∗ Zhiying Lv,∗∗,∗∗∗∗ Ping Huang,∗∗ Jinbiao Yuan,∗∗∗ and Hao Long∗∗∗∗∗,∗∗∗∗∗∗

References

  1. [1] J.A. Gon¸calves, and R. Henriques, UAV photogrammetry fortopographic monitoring of coastal areas, ISPRS Journal ofPhotogrammetry and Remote Sensing, 104, 2015, 101–111.
  2. [2] Y. Yan, Z. Lv, and R. Zhang, Fault evaluation of unmannedaerial vehicles power system with an improved fuzzy multi-ple attribute group decision-making, International Journal ofRobotics and Automation, 33(3), 2018, 284–292.
  3. [3] S. Harwin, and A. Lucieer, Assessing the accuracy of georef-erenced point clouds produced via multi-view stereopsis fromunmanned aerial vehicle (UAV) imagery, Remote Sensing, 4,2012, 1573–1599.
  4. [4] D. Turner, A. Lucieer, and S. de Jong, Time series analysis oflandslide dynamics using an unmanned aerial vehicle (UAV),Remote Sensing, 7, 2015, 1736–1757.
  5. [5] Y. Yan, Z. Lv, J. Yuan, and J. Chai, Analysis of power sourceof multirotor UAVs, International Journal of Robotics andAutomation, 34(5), 2019, 563–571.
  6. [6] X.Y. Zhang, X. Tang, J.L. Yang, and Z.Y. Lv, Quantita-tive three-way class-specific attribute reducts based on regionpreservations, International Journal of Approximate Reason-ing, 117, 2020, 96–121.
  7. [7] X.Y. Zhang, and D.Q. Miao, Three-way attribute reducts,International Journal of Approximate Reasoning, 88, 2017,401–434.
  8. [8] C. Jiang, and Y. Yao, Effectiveness measures in movement-based three-way decisions, Knowledge-Based Systems, 160,2018, 136–143.
  9. [9] Y.Y. Yao, Tri-level thinking: Models of three-way decision,International Journal of Machine Learning and Cybernetics,11, 2020, 947–959.
  10. [10] J.L. Yang, and Y.Y. Yao, Semantics of soft sets and three-waydecision with soft sets, Knowledge-Based Systems, 194, 2020,105538.
  11. [11] Y.Y. Yao, Three-way decision and granular computing, In-ternational Journal of Approximate Reasoning, 103, 2018,107–123.
  12. [12] H. Yu, L. Chen, and J. Yao, A three-way density peak clusteringmethod based on evidence theory, Knowledge-Based Systems,211, 2020, 106532.
  13. [13] Z.Y. Lv, L.W. Zheng, X.N. Liang, and X.Z. Liang, A fuzzymultiple attribute decision making method based on possibilitydegree, Journal of Intelligent & Fuzzy Systems, 31, 2016,787–794.
  14. [14] Y. Liu, Z.P. Fan, and X. Zhang, A method for large groupdecision-making based on evaluation information provided byparticipators from multiple groups, Information Fusion, 29,2016, 132–141.
  15. [15] M. Pesce, S. Terzi, R.I.M. Al-Jawasreh, et al., Selecting sus-tainable alternatives for cruise ships in Venice using multicri-teria decision analysis, Science of the Total Environment, 642,2018, 668–678.
  16. [16] S. Wan, S.Q. Li, and J.Y. Dong, A three-phase method forPythagorean fuzzy multi-attribute group decision making andapplication to haze management, Computers & IndustrialEngineering, 123, 2018, 348–363.
  17. [17] Z. Lv, T. Hang, and X. Liang, A method for fuzzy multi-attribute decision-making with preference to attribute, CAAITransactions on Intelligent Systems, 10(2), 2015, 227–233.
  18. [18] K. Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems,20, 1986, 87–96.
  19. [19] S. Das, S. Kar, and T. Pal, Roust decision making usingintuitionistic fuzzy numbers, Granular Computing, 2(1), 2017,41–54.
  20. [20] H. Zhang, L. Shu, and S. Liao, Intuitionistic fuzzy soft roughset and its application in decision making, Abstract and AppliedAnalysis, 4, 2014, 1–13.
  21. [21] C. Tan, A multi-criteria interval-valued intuitionistic fuzzygroup decision making with Choquet integral-based TOPSIS,Expert Systems with Applications, 38(4), 2011, 3023–3033.
  22. [22] R. Sahin, Fuzzy multi-criteria decision making method based onthe improved accuracy function for interval-valued intuitionisticfuzzy sets, Soft Computing, 20(7), 2016. 2557–2563.
  23. [23] S.M. Chen, S.H. Chen, and W.H. Tsai, Multiple attributegroup decision making based on interval-valued intuitionisticfuzzy aggregation operators and transformation techniques ofinterval-valued intuitionistic fuzzy values, Information Science,367–368, 2016, 418–442.
  24. [24] X.L. Zhang, and Z.S. Xu, Soft computing based on maximizingconsensus and fuzzy TOPSIS approach to interval-valued intu-itionistic fuzzy group decision making, Applied Soft Computing,26, 2015, 42–56.
  25. [25] X. Gou, Z. Xu, and H. Liao, Exponential operations of interval-valued intuitionistic fuzzy numbers, International Journal ofMachine Learning and Cybernetics, 7(3), 2016, 501–518.
  26. [26] T. Chen, A prioritized aggregation operator-based approachto multiple criteria decision making using interval-valued intu-itionistic fuzzy sets: a comparative perspective, InformationScience, 28, 2014, 97–112.
  27. [27] V. Lakshmana G. Nayagam, S. Jeevaraj, and P. Dhanasekaran,An intuitionistic fuzzy multi-criteria decision-making methodbased on non-hesitance score for interval-valued intuitionisticfuzzy sets, Soft Computing, 21, 2017, 7077–7082.
  28. [28] Z. Xu, and X. Gou, An overview of interval-valued intuitionis-tic fuzzy information aggregation and applications, GranularComputing, 2(1), 2017,13–39.
  29. [29] R. Sahin, Fuzzy multicriteria decision making method based onthe improved accuracy function for interval-valued intuitionisticfuzzy sets, Soft Computing, 20(7), 2015, 2557–2563.

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