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New Linguistic Z-Number Petri Nets for Knowledge Acquisition and Representation Under Large Group Environment

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Abstract

In this paper, we develop a new model named linguistic Z-number Petri nets for knowledge acquisition and representation in the large group environment. First, the linguistic Z-number production rules are introduced for knowledge representation, where truth degrees, threshold values, and certainty factors are described by linguistic Z-numbers. Subsequently, a knowledge acquisition approach is put forward to obtain the knowledge parameters of linguistic Z-number Petri nets based on a large group of experts. To reduce the complexity of knowledge reasoning, a simplification method is proposed to simplify the structure of linguistic Z-number Petri nets. Finally, a real case of chemical security risk assessment is provided to demonstrate the practicability and effectiveness of the proposed linguistic Z-number Petri net model. The results show that risk level of the given chemical plant is high and efficient actions should be taken to identify threat drivers and reduce security risk. Moreover, through a sensitivity analysis and a comparative analysis, it is concluded that the proposed linguistic Z-number Petri nets can represent experts’ complex and uncertain cognitive information comprehensively and acquire more precise and reasonable knowledge from domain experts.

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Acknowledgements

The authors are very grateful to the respected editor and the anonymous referees for their insightful and constructive comments, which helped to improve the overall quality of the paper. This work was supported by the Fundamental Research Funds for the Central Universities (Grant No. 22120220035).

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Authors and Affiliations

  1. School of Materials, Shanghai Dianji University, Shanghai, 201306, People’s Republic of China

    Hua Shi

  2. School of Economics and Management, Tongji University, Shanghai, 200092, People’s Republic of China

    Hu-Chen Liu & Jing-Hui Wang

  3. School of Management, Shanghai University, Shanghai, 200444, People’s Republic of China

    Xun Mou

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  1. Hua Shi
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  2. Hu-Chen Liu
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  4. Xun Mou
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Correspondence to Jing-Hui Wang.

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Shi, H., Liu, HC., Wang, JH. et al. New Linguistic Z-Number Petri Nets for Knowledge Acquisition and Representation Under Large Group Environment. Int. J. Fuzzy Syst. 24, 3483–3500 (2022). https://doi.org/10.1007/s40815-022-01341-9

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