Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks

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61 Citations (Scopus)


This paper proposes a new delay-dependent state estimator for Takagi-Sugeno (T-S) fuzzy delayed Hopfield neural networks. By employing a suitable Lyapunov-Krasovskii functional, a delay-dependent criterion is established to estimate the neuron states through available output measurements such that the dynamics of the estimation error is asymptotically stable. It is shown that the design of the proposed state estimator for such neural networks can be achieved by solving a linear matrix inequality (LMI), which can be easily facilitated by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.

Original languageEnglish
Pages (from-to)483-489
Number of pages7
JournalNonlinear Dynamics
Issue number3
Publication statusPublished - 2010 Aug
Externally publishedYes


  • Linear matrix inequality (LMI)
  • Lyapunov-Krasovskii stability theory
  • State estimation
  • Takagi-sugeno (T-S) fuzzy Hopfield neural networks

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Aerospace Engineering
  • Ocean Engineering
  • Mechanical Engineering
  • Applied Mathematics
  • Electrical and Electronic Engineering


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