Multi-task TSK fuzzy system modeling using inter-task correlation information

Yizhang Jiang, Zhaohong Deng, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)


The classical fuzzy system modeling methods have been typically developed for the single task modeling scene, which is essentially not in accordance with many practical applications where a multi-task problem must be considered for the given modeling task. Although a multi-task problem can be decomposed into many single-task sub-problems, the modeling results indeed tell us that the individual modeling approach will not be very suitable for multi-task problems due to the ignorance of the inter-task latent correlation between different tasks. In order to circumvent this shortcoming, a multi-task Takagi-Sugeno-Kang fuzzy system model is proposed based on the classical L2-norm Takagi-Sugeno-Kang fuzzy system in this paper. The proposed model cannot only take advantage of independent information of each task, but also make use of the inter-task latent correlation information effectively, resulting to obtain better generalization performance for the built fuzzy systems. Experiments on synthetic and real-world datasets demonstrate the applicability and distinctive performance of the proposed multi-task fuzzy system model in multi-task modeling scenarios.
Original languageEnglish
Pages (from-to)512-533
Number of pages22
JournalInformation Sciences
Publication statusPublished - 1 Jan 2015


  • Fuzzy modeling
  • Inter-task latent correlation
  • Multi-task learning
  • Sugeno-Kang fuzzy system
  • Takagi

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

Cite this