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State estimation for memristor-based neural networks with time-varying delays

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Abstract

This paper is concerned with the state estimation problem for a class of memristor-based neural networks with time-varying delay. A delay dependent condition is developed to estimate the neuron states through observed output measurements such that the error system is globally asymptotically stable. By constructing more effective Lyapunov functionals, and combining with Jensen integral inequality and free-weighting matrix approach, a less conservative sufficient condition for the existence of state estimator is formulated in terms of linear matrix inequality, which can be checked efficiently by using some standard numerical packages. Finally, a numerical example is given to demonstrate the effectiveness of the presented results.

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Authors and Affiliations

  1. Department of Mathematics and statistics, Chongqing University, Chongqing, 401331, China

    Hongzhi Wei & Chunrong Chen

  2. Department of Applied Mathematics, Yanshan University, Qinhuangdao, 066004, China

    Ruoxia Li

Authors
  1. Hongzhi Wei
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  2. Ruoxia Li
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  3. Chunrong Chen
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Correspondence to Hongzhi Wei.

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Wei, H., Li, R. & Chen, C. State estimation for memristor-based neural networks with time-varying delays. Int. J. Mach. Learn. & Cyber. 6, 213–225 (2015). https://doi.org/10.1007/s13042-014-0257-x

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  • DOI: https://doi.org/10.1007/s13042-014-0257-x

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