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Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays

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  • Guo, Runan
  • Xu, Shengyuan

Abstract

In this paper, the synchronization problem of complex-valued inertial neural networks is studied via sliding mode control (SMC). Both mixed time-varying delays and unknown control disturbances are considered. In the absence of the equivalent transformations of real- and complex-valued systems, the systems are analyzed as an entirety form in complex domain. A disturbance observer is designed to estimate the unknown control disturbance. By constructing suitable integral switching surface function and innovative Lyapunov–Krasovskii functionals, a delay-dependent synchronization criterion is proposed on the basis of linear matrix inequality technique. An efficient SMC law based on the disturbance observer is designed, and the accessibility analysis of the predefined switching surface is provided. Eventually, numerical verification based on two types of activation functions, as well as the superiority and practicability analysis are provided.

Suggested Citation

  • Guo, Runan & Xu, Shengyuan, 2023. "Observer-based sliding mode synchronization control of complex-valued neural networks with inertial term and mixed time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    • Handle: RePEc:eee:apmaco:v:442:y:2023:i:c:s0096300322008293
    DOI: 10.1016/j.amc.2022.127761
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    References listed on IDEAS

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    4. Vadivel, R. & Hammachukiattikul, Porpattama & Rajchakit, G. & Syed Ali, M. & Unyong, Bundit, 2021. "Finite-time event-triggered approach for recurrent neural networks with leakage term and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 182(C), pages 765-790.
    5. Vadivel, R. & Hammachukiattikul, P. & Gunasekaran, Nallappan & Saravanakumar, R. & Dutta, Hemen, 2021. "Strict dissipativity synchronization for delayed static neural networks: An event-triggered scheme," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
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