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Observer-based finite-time bounded analysis for switched inertial recurrent neural networks under the PDT switching law

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  • Huang, Zhengguo
  • Xia, Jianwei
  • Wang, Jing
  • Wang, Jian
  • Shen, Hao

Abstract

In this note, the observer-based finite-time boundedness analysis issue for the switched inertial recurrent neural networks ( SIRNNs) is investigated deeply. The switching law, persistent dwell-time, with more generality and universality is employed. The first target is to develop a switched estimation system (SES) to obtain the states from the output of the researched open-loop SIRNNs. Thereafter, based on the before-mentioned SES, the resulting switched estimation error system (SEES) without the extrinsic disturbance, along with the closed-loop SIRNNs under state feedback controller are constructed. Furthermore, the sufficient conditions that the exponential stability for the SEES and the finite-time boundedness for the closed-loop SIRNNs are established simultaneously. The relevant estimator and controller gains are deduced by a straightforward decoupling manner. Ultimately, the feasibility of the method proposed is clarified and illustrated via a numerical example.

Suggested Citation

  • Huang, Zhengguo & Xia, Jianwei & Wang, Jing & Wang, Jian & Shen, Hao, 2020. "Observer-based finite-time bounded analysis for switched inertial recurrent neural networks under the PDT switching law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 538(C).
    • Handle: RePEc:eee:phsmap:v:538:y:2020:i:c:s0378437119315389
    DOI: 10.1016/j.physa.2019.122699
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    References listed on IDEAS

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    Cited by:

    1. Joby, Maya & Santra, Srimanta & Anthoni, S. Marshal, 2021. "Finite-time contractive boundedness of extracorporeal blood circulation process," Applied Mathematics and Computation, Elsevier, vol. 388(C).

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