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Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach

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Abstract

In this paper, exponential anti-synchronization in mean square of an uncertain memristor-based neural network is studied. The uncertain terms include non-modeled dynamics with boundary and stochastic perturbations. Based on the differential inclusions theory, linear matrix inequalities, Gronwall’s inequality and adaptive control technique, an adaptive controller with update laws is developed to realize the exponential anti-synchronization. Adaptive controller can adjust itself behavior to get the best performance, according to the environment is changing or the environment has changed, which has the ability to adapt to environmental change. Furthermore, a numerical example is provided to validate the effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Science, Beijing University of Posts and Telecommunications, Beijing, 100876, P.R. China

    Hui Zhao & Jinghua Xiao

  2. Information Security Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, P.R. China

    Lixiang Li, Haipeng Peng & Yixian Yang

  3. Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany

    Jürgen Kurths

Authors
  1. Hui Zhao
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  2. Lixiang Li
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  3. Haipeng Peng
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  4. Jürgen Kurths
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  5. Jinghua Xiao
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  6. Yixian Yang
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Corresponding author

Correspondence to Lixiang Li.

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Zhao, H., Li, L., Peng, H. et al. Anti-synchronization for stochastic memristor-based neural networks with non-modeled dynamics via adaptive control approach. Eur. Phys. J. B 88, 109 (2015). https://doi.org/10.1140/epjb/e2015-50798-9

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  • DOI: https://doi.org/10.1140/epjb/e2015-50798-9

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