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Delay-Dependent Passivity and Stability Analysis for a Class of Memristor-Based Neural Networks with Time Delay in the Leakage Term

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Abstract

This paper is concerned with the problem of passivity and stability analysis for a class of memristor-based neural networks with leakage delay and state-dependent switched memductance functions. By combining differential inclusions with set-valued maps and constructing a proper Lyapunov–Krasovskii functional, delay-dependent criteria in terms of linear matrix inequalities are obtained for the passivity of the memristive neural networks. Meanwhile, based on the derived criteria, stability criteria are obtained for the networks via Barbalat’s lemma. Finally, a numerical example is given to illustrate the feasibility of the theoretical results.

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Authors and Affiliations

  1. Institute of Applied Mathematics, Shijiazhuang Mechanical Engineering College, Shijiazhuang, 050003, Hebei, People’s Republic of China

    Jian Liu & Rui Xu

Authors
  1. Jian Liu
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  2. Rui Xu
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Correspondence to Jian Liu.

Additional information

Supported by NNSF of China (11371368, 11071254) and HEBNSF of China (A2014506015).

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Liu, J., Xu, R. Delay-Dependent Passivity and Stability Analysis for a Class of Memristor-Based Neural Networks with Time Delay in the Leakage Term. Neural Process Lett 46, 467–485 (2017). https://doi.org/10.1007/s11063-017-9594-6

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  • DOI: https://doi.org/10.1007/s11063-017-9594-6

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