An Extended Takagi-Sugeno-Kang Inference System (TSK+) with Fuzzy Interpolation and Its Rule Base Generation

Jie Li, Longzhi Yang, Yanpeng Qu, Graham Sexton

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    Abstract

    A rule base covering the entire input domain is required for the conventional Mamdani inference and Takagi-Sugeno-Kang (TSK) inference. Fuzzy interpolation enhances conventional fuzzy rule inference systems by allowing the use of sparse rule bases by which certain inputs are not covered. Given that almost all of the existing fuzzy interpolation approaches were developed to support the Mamdani inference, this paper presents a novel fuzzy interpolation approach that extends the TSK inference. This paper also proposes a data-driven rule base generation method to support the extended TSK inference system. The proposed system enhances the conventional TSK inference in two ways: 1) workable with incomplete or unevenly distributed data sets or incomplete expert knowledge that entails only a sparse rule base, and 2) simplifying complex fuzzy inference systems by using more compact rule bases for complex systems without the sacrificing of system performance. The experimentation shows that the proposed system overall outperforms the existing approaches with the utilisation of smaller rule bases.
    Original languageEnglish
    Pages (from-to)3155-3170
    Number of pages16
    JournalSoft Computing
    Volume22
    Issue number10
    Early online date20 Nov 2017
    DOIs
    Publication statusPublished - 2018

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