Takagi-Sugeno fuzzy hopfield neural networks for H nonlinear system identification

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27 Citations (Scopus)


In this paper, we propose a new H weight learning algorithm (HWLA) for nonlinear system identification via Takagi.Sugeno (T.S) fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, for the first time, the HWLA fornonlinear system identification is presented to reduce the effect of disturbance to an H norm constraint. The HWLA can be obtained by solving a convex optimization problem which is represented in terms of linear matrix inequality (LMI). An illustrative example is given to demonstrate the effectiveness of the proposed identification scheme.

Original languageEnglish
Pages (from-to)59-70
Number of pages12
JournalNeural Processing Letters
Issue number1
Publication statusPublished - 2011 Aug
Externally publishedYes


  • H nonlinear system identification
  • Linear matrix inequality (LMI)
  • Lyapunov stabilitytheory
  • Takagi-Sugeno fuzzy Hopfield neural networks
  • Weight learning algorithm

ASJC Scopus subject areas

  • Software
  • General Neuroscience
  • Computer Networks and Communications
  • Artificial Intelligence


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