Concise Fuzzy System Modeling Integrating Soft Subspace Clustering and Sparse Learning

Peng Xu, Zhaohong Deng, Chen Cui, Te Zhang, Kup Sze Choi, Suhang Gu, Jun Wang, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)


The superior interpretability and uncertainty modeling ability of Takagi-Sugeno-Kang fuzzy system (TSK FS) make it possible to describe complex nonlinear systems intuitively and efficiently. However, classical TSK FS usually adopts the whole feature space of the data for model construction, which can result in lengthy rules for high-dimensional data and lead to degeneration in interpretability. Furthermore, for highly nonlinear modeling task, it is usually necessary to use a large number of rules which further weaken the clarity and interpretability of TSK FS. To address these issues, an enhanced soft subspace clustering (ESSC) and sparse learning (SL) based concise zero-order TSK FS construction method, called ESSC-SL-CTSK-FS, is proposed in this paper by integrating the techniques of ESSC and SL. In this method, ESSC is used to generate the antecedents and various sparse subspaces for different fuzzy rules, whereas SL is used to optimize the consequent parameters of the fuzzy rules based on which the number of fuzzy rules can be effectively reduced. Finally, the proposed ESSC-SL-CTSK-FS method is used to construct concise zero-order TSK FS that can explain the scenes in high-dimensional data modeling more clearly and easily. Experiments are conducted on various real-world datasets to confirm the advantages.

Original languageEnglish
Article number8626516
Pages (from-to)2176-2189
Number of pages14
JournalIEEE Transactions on Fuzzy Systems
Issue number11
Publication statusPublished - Nov 2019


  • Enhanced soft subspace clustering
  • high-dimensional data
  • interpretability
  • sparse learning
  • Takagi-Sugeno-Kang (TSK) fuzzy system

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


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