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An Inexact Reasoning Method Based on Sugeno Integral

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Fuzzy Sets and Operations Research (ICFIE 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 872))

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Abstract

Fuzzy rules, which are important tools in the fuzzy reasoning, have been widely used in expert systems to represent fuzzy and uncertain concepts. Using the fuzzy set as an interior representation can make the reasoning accord with the man’s thought better. To represent the fuzziness and uncertainty more effectively and make the result more reasonable, some knowledge representation parameters such as threshold value, certain factor, local weight and global weight are introduced. When the interaction exists among fuzzy proposition in a fuzzy rule set, the parameter weight is displayed by the fuzzy measure. The paper proposes an inexact reasoning method based on matrix transformation, the interaction exists among fuzzy propositions and the confidence level of the rules are all considered. The method is mainly used in the incomplete inductive reasoning.

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Acknowledgements

Thanks to the supported by the International Science and Technology Cooperation Foundation of China (Grant No. 2012DFA11270) by Natural Science Foundation of Hainan (Grant No. 117123).

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Authors and Affiliations

  1. School of Mathematics and Statistics, Hainan Normal University, HaiKou, 571158, China

    Jun Shen & De-jun Peng

  2. School of Information Science and Technology, Hainan Normal University, HaiKou, 571158, China

    Sheng-quan Ma

Authors
  1. Jun Shen
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  2. De-jun Peng
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  3. Sheng-quan Ma
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Corresponding author

Correspondence to Jun Shen .

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Editors and Affiliations

  1. Foshan University, Foshan, Guangdong, China

    Bing-Yuan Cao

  2. School of Mathematics and Information Science, Guangzhou University, Guangzhou, China

    Yu-Bin Zhong

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Thanks to Professor Sheng-quan Ma’s recommendation of Hainan Normal University in China.

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Shen, J., Peng, Dj., Ma, Sq. (2019). An Inexact Reasoning Method Based on Sugeno Integral. In: Cao, BY., Zhong, YB. (eds) Fuzzy Sets and Operations Research. ICFIE 2017. Advances in Intelligent Systems and Computing, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-02777-3_4

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