Knowledge-leverage-based TSK fuzzy system modeling

Zhaohong Deng, Yizhang Jiang, Kup Sze Choi, Fu Lai Korris Chung, Shitong Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

125 Citations (Scopus)


Classical fuzzy system modeling methods consider only the current scene where the training data are assumed to be fully collectable. However, if the data available from the current scene are insufficient, the fuzzy systems trained by using the incomplete datasets will suffer from weak generalization capability for the prediction in the scene. In order to overcome this problem, a knowledge-leverage-based fuzzy system (KL-FS) is studied in this paper from the perspective of transfer learning. The KL-FS intends to not only make full use of the data from the current scene in the learning procedure, but also effectively leverage the existing knowledge from the reference scenes. Specifically, a knowledge-leverage-based Takagi-Sugeno-Kang-type Fuzzy System (KL-TSK-FS) is proposed by integrating the corresponding knowledge-leverage mechanism. The new fuzzy system modeling technique is evaluated through experiments on synthetic and real-world datasets. The results demonstrate that KL-TSK-FS has better performance and adaptability than the traditional fuzzy modeling methods in scenes with insufficient data.
Original languageEnglish
Article number6502723
Pages (from-to)1200-1212
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number8
Publication statusPublished - 24 Apr 2013


  • Fuzzy modeling
  • fuzzy systems (FS)
  • knowledge leverage (KL)
  • missing data
  • transfer learning

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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