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Exponential operational laws and new aggregation operators of intuitionistic Fuzzy information based on Archimedean T-conorm and T-norm

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Abstract

Atanassov extended the fuzzy set to intuitionistic fuzzy set (IFS) whose basic components are intuitionistic fuzzy numbers (IFNs). IFSs and IFNs can depict the fuzzy characteristics of the objects comprehensively, and lots of operational laws have been introduced to facilitate the use of IFSs and IFNs for solving the practical problems under intuitionistic fuzzy environments. As a supplement of the existing operational laws, we define the exponential operational laws of IFSs and IFNs based on Archimedean t-conorm and t-norm (EOL-IFS-A and EOL-IFN-A), which can be considered as the more general forms of the original exponential operational law. After that, we study the properties of the EOL-IFS-A and EOL-IFN-A. Then, we develop an approach for multiple criteria decision making with intuitionistic fuzzy information. Finally, we give an example to illustrate the application of the developed approach, and make a detailed comparison with the existing method so as to show the advantages of our approach.

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Acknowledgements

The authors would like to thank the editors and the anony- mous referees for their insightful and constructive comments and suggestions that have led to this improved version of the pa- per. The work was supported in part by the National Natural Science Foundation of China (Nos. 71571123, 71501135, 61273209 and 71532007), the China Postdoctoral Science Foundation (No. 2016T90863), and the Central University Basic Scientific Research Business Expenses Project (Nos. skgt201501 and skqy201649).

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Authors and Affiliations

  1. Business School, Sichuan University, Chengdu, 610064, China

    Xue Luo, Zeshui Xu & Xunjie Gou

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  1. Xue Luo

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  2. Zeshui Xu

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  3. Xunjie Gou

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Correspondence toZeshui Xu.

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Luo, X., Xu, Z. & Gou, X. Exponential operational laws and new aggregation operators of intuitionistic Fuzzy information based on Archimedean T-conorm and T-norm.Int. J. Mach. Learn. & Cyber.9, 1261–1269 (2018). https://doi.org/10.1007/s13042-016-0632-x

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