H State Estimation for Takagi-Sugeno Fuzzy Delayed Hopfield Neural Networks

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12 Citations (Scopus)


This paper presents a new H state estimator for Takagi-Sugeno fuzzy delayed Hopfield neural networks. Based on Lyapunov-Krasovskii stability approach, a delay-dependent criterion is proposed to ensure that the resulting estimation error system is asymptotically stable with a guaranteed H performance. The proposed H state estimator can be realized by solving a linear matrix inequality (LMI) problem. An illustrative numerical example is given to verify the effectiveness of the proposed H state estimator.

Original languageEnglish
Pages (from-to)855-862
Number of pages8
JournalInternational Journal of Computational Intelligence Systems
Issue number5
Publication statusPublished - 2011 Sept
Externally publishedYes


  • H state estimation
  • Lyapunov-Krasovskii stability theory
  • Takagi-Sugeno fuzzy Hopfield neural networks
  • linear matrix inequality (LMI)

ASJC Scopus subject areas

  • General Computer Science
  • Computational Mathematics


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