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

Choon Ki Ahn

doi:10.1080/18756891.2011.9727836

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

Choon Ki Ahn

Research output: Contribution to journal › Article › peer-review

12 Citations (Scopus)

Abstract

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 language	English
Pages (from-to)	855-862
Number of pages	8
Journal	International Journal of Computational Intelligence Systems
Volume	4
Issue number	5
DOIs	https://doi.org/10.1080/18756891.2011.9727836
Publication status	Published - 2011 Sept
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

Computer Science(all)
Computational Mathematics

Access to Document

10.1080/18756891.2011.9727836

Cite this

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abstract = "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.",

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