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Design of state estimator for uncertain neural networks via the integral-inequality method

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Abstract

The issue of state estimation is studied for a class of neural networks with norm-bounded parameter uncertainties and time-varying delay. Some new linear matrix inequality (LMI) representations of delay-dependent stability criteria are presented for the existence of the desired estimator for all admissible parametric uncertainties. The proposed method is based on the S-procedure and an extended integral inequality which can be deduced from the well-known Leibniz–Newton formula and Moon’s inequality. The results extend some models reported in the literature and improve conservativeness of those in the case that the derivative of the time-varying delay is assumed to be less than one. Two numerical examples are given to show the effectiveness and superiority of the results.

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Authors and Affiliations

  1. College of Communication and Control Engineering, Jiangnan University, 1800 Lihu Rd., Wuxi, Jiangsu, 214122, China

    Xuyang Lou & Baotong Cui

  2. CSIRO Division of Mathematical and Information Sciences, Urrbrae, South Australia, 5064, Australia

    Xuyang Lou

Authors
  1. Xuyang Lou
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  2. Baotong Cui
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Correspondence to Xuyang Lou.

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Lou, X., Cui, B. Design of state estimator for uncertain neural networks via the integral-inequality method. Nonlinear Dyn 53, 223–235 (2008). https://doi.org/10.1007/s11071-007-9310-6

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  • DOI: https://doi.org/10.1007/s11071-007-9310-6

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