SVD-based complexity reduction to TS fuzzy models

Péter Baranyi, Yeung Yam, Annamária R. Várkonyi-Kóczy, Ron J. Patton, Pal Michelberger, Masaharu Sugiyama

Research output: Contribution to journalArticle

39 Citations (Scopus)


One of the typical important criteria to be considered in real-time control applications is the computational complexity of the controllers, observers, and models applied. In this paper, a singular value decomposition (SVD)-based complexity reduction technique is proposed for Takagi Sugeno (TS) fuzzy models. The main motivation is that the TS fuzzy model has exponentially growing computational complexity with the improvement of its approximation property through, as usually practiced, increasing the density of antecedent terms. The reduction technique proposed here is capable of defining the contribution of each local linear model included in the TS fuzzy model, which serves to remove the weakly contributing ones as according to a given threshold. Reducing the number of models leads directly to the computational complexity reduction. This work also includes a number of numerical and application examples.

Original languageEnglish
Pages (from-to)433-443
Number of pages11
JournalIEEE Transactions on Industrial Electronics
Issue number2
Publication statusPublished - Apr 1 2002


  • Anytime systems
  • Complexity reduction
  • Fuzzy rule base reduction
  • Singular value decomposition (SVD)
  • TS fuzzy model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'SVD-based complexity reduction to TS fuzzy models'. Together they form a unique fingerprint.

  • Cite this