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Dissipativity of the stochastic Markovian switching CVNNs with randomly occurring uncertainties and general uncertain transition rates

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  • Qiang Li
  • Jinling Liang

Abstract

The robust dissipativity problem is analysed in this article for the Markovian switching complex-valued neural networks perturbed by stochastic noises, where the transition rates of the Markovian switching are uncertain which comprise two categories: completely unknown or unknown but with known upper/lower bounds. The randomly occurring system uncertainties are governed by certain mutually independent Bernoulli-distributed white sequences, which might reflect more realistic dynamical behaviours of the switching network. Based on the generalised It $\hat {o} $oˆ's formula in complex form as well as certain stochastic analysis methods, several mode-dependent dissipativity/passivity criteria are obtained in terms of complex matrix inequalities. Finally, illustrative examples are provided to demonstrate feasibility of the derived results.

Suggested Citation

  • Qiang Li & Jinling Liang, 2020. "Dissipativity of the stochastic Markovian switching CVNNs with randomly occurring uncertainties and general uncertain transition rates," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(6), pages 1102-1118, April.
    • Handle: RePEc:taf:tsysxx:v:51:y:2020:i:6:p:1102-1118
    DOI: 10.1080/00207721.2020.1752418
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    Cited by:

    1. Li, Qiang & Liang, Jinling, 2022. "Non-fragile asynchronous state estimation for Markovian switching CVNs with partly accessible mode detection: The discrete-time case," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    2. Liu, An & Huang, Xia & Fan, Yingjie & Wang, Zhen, 2021. "A control-interval-dependent functional for exponential stabilization of neural networks via intermittent sampled-data control," Applied Mathematics and Computation, Elsevier, vol. 411(C).

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