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

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Abstract

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.

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Authors and Affiliations

  1. Division of Electronics and Control Engineering, Wonkwang University, Iksan, Korea

    Choon Ki Ahn

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  1. Choon Ki Ahn
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Correspondence to Choon Ki Ahn.

Additional information

This paper was supported by Wonkwang University in 2010.

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Ahn, C.K. Delay-dependent state estimation for T-S fuzzy delayed Hopfield neural networks. Nonlinear Dyn 61, 483–489 (2010). https://doi.org/10.1007/s11071-010-9664-z

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  • DOI: https://doi.org/10.1007/s11071-010-9664-z

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