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Non-Fragile H ∞ Asynchronous State Estimation for Delayed Markovian Jumping NNs with Stochastic Disturbance

Author

Listed:
  • Lan Wang

    (Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China
    These authors contributed equally to this work.)

  • Juping Tang

    (Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China
    These authors contributed equally to this work.)

  • Qiang Li

    (School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China)

  • Xianwei Yang

    (Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China)

  • Haiyang Zhang

    (School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China)

Abstract

This article focuses on tackling the non-fragile H ∞ asynchronous estimation problem for delayed Markovian jumping neural networks (NNs) featuring stochastic disturbance. To more accurately reflect real-world scenarios, external random disturbances with known statistical characteristics are incorporated. Through the integration of stochastic analysis theory and Lyapunov stability techniques, as well as several matrix constraints formulas, some sufficient and effective results are addressed. These criteria ensure that the considered NNs achieve anticipant H ∞ stability in line with an external disturbance mitigation level. Meanwhile, the expected estimator gains will be explicitly constructed by dealing with corresponding matrix constraints. To conclude, a numerical simulation example is offered to showcase workability and validity of the formulated estimation method.

Suggested Citation

  • Lan Wang & Juping Tang & Qiang Li & Xianwei Yang & Haiyang Zhang, 2025. "Non-Fragile H ∞ Asynchronous State Estimation for Delayed Markovian Jumping NNs with Stochastic Disturbance," Mathematics, MDPI, vol. 13(15), pages 1-17, July.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:15:p:2452-:d:1713044
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    References listed on IDEAS

    as
    1. Abdurahman, Abdujelil & Abudusaimaiti, Mairemunisa & Jiang, Haijun, 2023. "Fixed/predefined-time lag synchronization of complex-valued BAM neural networks with stochastic perturbations," Applied Mathematics and Computation, Elsevier, vol. 444(C).
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