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The Fuzzy Integral Evaluation Method Based on Feature Weighting for the Level of Complex Social Development

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Cyberspace Safety and Security (CSS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11982))

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Abstract

This paper puts the thought of the dichotomy of grid used in clustering center extraction algorithm based on grid. The structure optimization of fuzzy clustering neural network model is realized. This paper takes the classification of social development level as an example to verify that this structure has the advantages of overcoming the slow convergence speed and solving the problem of clustering dead point. The paper analyses various factors affecting the comprehensive development level of society, and quotes the concepts of fuzzy measure and fuzzy integral, puts forward the evaluation method of fuzzy integral for the comprehensive development level of society, and establishes the corresponding evaluation model of multi-index and multi-level fuzzy integral for the comprehensive development level of society.

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References

  1. Tahmoresnezhad, J., Hashemi, S.: Turk. J. Electr. Eng. Comput. Sci. 25(1), 292–307 (2017). https://doi.org/10.3906/elk-1503-245

    Article  Google Scholar 

  2. Amorim, R.: J. Classif. 33(2), 210–242 (2016). https://doi.org/10.1007/s00357-016-9208-4

    Article  Google Scholar 

  3. Baghmisheh, M., Ezzati, R.: Error estimation and numerical solution of nonlinear fuzzy Fredholm integral equations of the second kind using triangular functions. J. Intell. Fuzzy Syst. 30, 639–649 (2016)

    Article  Google Scholar 

  4. Sadatrasoul, S.M., Ezzati, R.: Numerical solution of two-dimensional nonlinear Hammerstein fuzzy integral equations based on optimal fuzzy quadrature formula. J. Comput. Appl. Math. 292, 430–446 (2016)

    Article  MathSciNet  Google Scholar 

  5. Chu, J.F., Liu, X.W., Wang, Y.M., Chin, K.S.: A group decision making model considering both the additive consistency and group consensus of intuitionistic fuzzy preference relations. Comput. Ind. Eng. 101, 227–242 (2016)

    Article  Google Scholar 

  6. Chen, S.M., Kao, P.Y.: TAIEX forecasting based on fuzzy time series, particle swarm optimization techniques and support vector machines. Inf. Sci. 247(Suppl. C), 62–71 (2013)

    Article  MathSciNet  Google Scholar 

  7. Das, S., Kar, S., Pal, T.: Robust decision making using intuitionistic fuzzy numbers. Granul. Comput. 2(1), 41–54 (2016)

    Article  Google Scholar 

  8. Babolian, E., Sadeghi Goghary, H., Abbasbandy, S.: Numerical solution of linear Fredholm fuzzy integral equations of the second kind by Adomian method. Appl. Math. Comput. 161, 733–744 (2005)

    MathSciNet  MATH  Google Scholar 

  9. Bede, B., Gal, S.G.: Quadrature rules for integrals of fuzzy-number-valued functions. Fuzzy Sets Syst. 145, 359–380 (2004)

    Article  MathSciNet  Google Scholar 

  10. Cai, M.J., Li, Q.G., Lang, G.M.: Shadowed sets of dynamic fuzzy sets. Granul. Comput. 2(2), 85–94 (2017)

    Article  Google Scholar 

  11. Chatterjee, K., Kar, S.: Unified granular-number based AHP-VIKOR multi-criteria decision framework. Granul. Comput. 2(3), 199–221 (2017)

    Article  Google Scholar 

  12. Chen, N., Xu, Z.S., Xia, M.M.: Interval-valued hesitant preference relations and their applications to group decision making. Knowl. Based Syst. 37(2), 528–540 (2013)

    Article  Google Scholar 

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Acknowledgement

This work is supported by Shandong Natural Science Foundation (ZR2013FL020), and Shandong Technology and Business University internal scientific research projects (04010621).

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Authors and Affiliations

  1. School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China

    Meifang Du

  2. School of Information Science and Engineering, Qufu Normal University, Rizhao, China

    Haojia Zhu

Authors
  1. Meifang Du
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  2. Haojia Zhu
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Corresponding author

Correspondence to Meifang Du .

Editor information

Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Beihang University, Beijing, China

    Xiao Zhang

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Du, M., Zhu, H. (2019). The Fuzzy Integral Evaluation Method Based on Feature Weighting for the Level of Complex Social Development. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11982. Springer, Cham. https://doi.org/10.1007/978-3-030-37337-5_36

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  • DOI: https://doi.org/10.1007/978-3-030-37337-5_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37336-8

  • Online ISBN: 978-3-030-37337-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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