Interval Type-2 TSK+ Fuzzy Inference System

Jie Li, Longzhi Yang, Xin Fu, Chao Fei, Yanpeng Qu

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

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    Abstract

    Type-2 fuzzy sets and systems can better handle
    uncertainties compared to its type-1 counterpart, and the widely
    applied Mamdani and TSK fuzzy inference approaches have
    been both extended to support interval type-2 fuzzy sets. Fuzzy
    interpolation enhances the conventional Mamdani and TKS
    fuzzy inference systems, which not only enables inferences
    when inputs are not covered by an incomplete or sparse
    rule base but also helps in system simplification for very
    complex problems. This paper extends the recently proposed
    fuzzy interpolation approach TSK+ to allow the utilization of
    interval type-2 TSK fuzzy rule bases. One illustrative case based
    on an example problem from the literature demonstrates the
    working of the proposed system, and the application on the cart
    centering problem reveals the power of the proposed system.
    The experimental investigation confirmed that the proposed
    approach is able to perform fuzzy inferences using either dense
    or sparse interval type-2 TSK rule bases with promising results
    generated.
    Original languageEnglish
    Title of host publication2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
    PublisherIEEE
    Pages1-8
    Number of pages8
    ISBN (Electronic)9781509060207
    DOIs
    Publication statusPublished - 15 Oct 2018
    Event2018 IEEE International Conference on Fuzzy Systems - Rio de Janeiro, Brazil
    Duration: 8 Jul 201813 Jul 2018

    Conference

    Conference2018 IEEE International Conference on Fuzzy Systems
    Abbreviated titleFUZZ- IEEE 2018
    CountryBrazil
    CityRio de Janeiro
    Period8/07/1813/07/18

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  • Cite this

    Li, J., Yang, L., Fu, X., Fei, C., & Qu, Y. (2018). Interval Type-2 TSK+ Fuzzy Inference System. In 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-8). IEEE. https://doi.org/10.1109/FUZZ-IEEE.2018.8491448