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Refined Expected Value Decision Rules under Orthopair Fuzzy Environment

Author

Listed:
  • Yige Xue

    (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China)

  • Yong Deng

    (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China)

Abstract

Refined expected value decision rules can refine the calculation of the expected value and make decisions by estimating the expected values of different alternatives, which use many theories, such as Choquet integral, PM function, measure and so on. However, the refined expected value decision rules have not been applied to the orthopair fuzzy environment yet. To address this issue, in this paper we propose the refined expected value decision rules under the orthopair fuzzy environment, which can apply the refined expected value decision rules on the issues of decision making that is described in the orthopair fuzzy environment. Numerical examples were applied to verify the availability and flexibility of the new refined expected value decision rules model. The experimental results demonstrate that the proposed model can apply refined expected value decision rules in the orthopair fuzzy environment and solve the decision making issues with the orthopair fuzzy environment successfully.

Suggested Citation

  • Yige Xue & Yong Deng, 2020. "Refined Expected Value Decision Rules under Orthopair Fuzzy Environment," Mathematics, MDPI, vol. 8(3), pages 1-14, March.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:3:p:442-:d:334088
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    References listed on IDEAS

    as
    1. Wei Liu & Tao Wang & Tianlei Zang & Zhu Huang & Jun Wang & Tao Huang & Xiaoguang Wei & Chuan Li, 2020. "A Fault Diagnosis Method for Power Transmission Networks Based on Spiking Neural P Systems with Self-Updating Rules considering Biological Apoptosis Mechanism," Complexity, Hindawi, vol. 2020, pages 1-18, January.
    2. Ping Wang & Jie Wang & Guiwu Wei & Cun Wei, 2019. "Similarity Measures of q-Rung Orthopair Fuzzy Sets Based on Cosine Function and Their Applications," Mathematics, MDPI, vol. 7(4), pages 1-23, April.
    3. Mo, Hongming & Deng, Yong, 2019. "Identifying node importance based on evidence theory in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 529(C).
    4. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.
    5. Shang Gao & Yong Deng, 2019. "An evidential evaluation of nuclear safeguards," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    6. Deng, Wei & Deng, Yong, 2018. "Entropic methodology for entanglement measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 693-697.
    7. Wen, Tao & Deng, Yong, 2020. "The vulnerability of communities in complex networks: An entropy approach," Reliability Engineering and System Safety, Elsevier, vol. 196(C).
    8. Xu, Paiheng & Zhang, Rong & Deng, Yong, 2018. "A novel visibility graph transformation of time series into weighted networks," Chaos, Solitons & Fractals, Elsevier, vol. 117(C), pages 201-208.
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