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Weighted Fuzzy Rules Based on Implicational Quantifiers

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Integrated Uncertainty in Knowledge Modelling and Decision Making (IUKM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14375))

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Abstract

In this paper, we explore the use of General Unary Hypotheses Automaton (GUHA) quantifiers, explicitly implicational quantifiers, for analyzing specific relational dependencies. We discuss their suitability in fuzzy modeling and demonstrate their integration with appropriate fuzzy rules to create a new class of weighted fuzzy rules. This study contributes to the advancement of fuzzy modeling and offers a framework for further research and practical applications.

This research was supported by the Czech Science Foundation project No. 23-06280S.

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Authors and Affiliations

  1. University of Ostrava, CE IT4Innovations, 30. dubna 22, 701 03, Ostrava 1, Czech Republic

    Martina Daňková

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  1. Martina Daňková
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Correspondence to Martina Daňková .

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Editors and Affiliations

  1. Japan Advanced Inst of Science and Technology, Nomi, Ishikawa, Japan

    Van-Nam Huynh

  2. Vietnam National University, Ho Chi Minh City, Vietnam

    Bac Le

  3. Osaka Metropolitan University, Sakai, Osaka, Japan

    Katsuhiro Honda

  4. Osaka University, Toyonaka, Osaka, Japan

    Masahiro Inuiguchi

  5. Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

    Youji Kohda

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Daňková, M. (2023). Weighted Fuzzy Rules Based on Implicational Quantifiers. In: Huynh, VN., Le, B., Honda, K., Inuiguchi, M., Kohda, Y. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2023. Lecture Notes in Computer Science(), vol 14375. Springer, Cham. https://doi.org/10.1007/978-3-031-46775-2_3

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  • DOI: https://doi.org/10.1007/978-3-031-46775-2_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-46774-5

  • Online ISBN: 978-3-031-46775-2

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