Optical Memory and Neural Networks

, Volume 28, Issue 2, pp 65–81 | Cite as

Passivity and Passification of Dynamic Memristor Neural Networks with Delays Operating in the Flux-Charge Domain

  • Jie Liu
  • Huaiqin WuEmail author


In this paper, the passivity and passification issue are considered for a class of dynamic memristor neural networks (DMNNs) with delays. Different from the models with respect to memristive neural networks in the literature, the flux-controlled dynamic memristors are used in the neurons and finite concentrated delays are accounted for in the interconnections. With the construction of suitable Lyapunov-Krasovskii functional (LKF), a novel passivity criteria, which involves the interconnection matrix, the delayed interconnection matrix and nonlinear memristor, is addressed in the form of linear matrix inequalities (LMIs) in the flux-charge domain. In addition, for state feedback passification, two procedures for designing passification controllers are proposed in terms of LMIs to guarantee that the considered DMNNs are passive. Finally, two examples are provided to illustrate the validity of the theoretical results.


memristor neural networks Lyapunov-Krasovskii functional passification passivity linear matrix inequality 


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Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  1. 1.School of Science, Yanshan UniversityQinhuangdaoChina

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