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Input-to-State Stability Analysis for Stochastic Mixed Time-Delayed Neural Networks with Hybrid Impulses

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  • Chi Zhao
  • Yinfang Song
  • Quanxin Zhu
  • Kaibo Shi
  • Eric Campos

Abstract

This paper investigates the mean-square exponential input-to-state stability (MEISS) of stochastic mixed time-delayed neural networks with hybrid impulses. A generalized comparison principle is introduced and a new inequality about the solution of an impulsive differential equation is established. Moreover, by utilizing the proposed inequality and average impulsive interval approach based on different kinds of impulsive sequences, some novel criteria on MEISS are established. When the external input is removed, several conclusions on mean-square exponential stability (MES) are also derived. Unusually, the hybrid impulses including destabilizing and stabilizing impulses have been taken into account in the presented system. Finally, two simulation examples are provided to demonstrate the validity of our theoretical results.

Suggested Citation

  • Chi Zhao & Yinfang Song & Quanxin Zhu & Kaibo Shi & Eric Campos, 2022. "Input-to-State Stability Analysis for Stochastic Mixed Time-Delayed Neural Networks with Hybrid Impulses," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-13, January.
    • Handle: RePEc:hin:jnlmpe:6135390
    DOI: 10.1155/2022/6135390
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    Cited by:

    1. Chunxiang Li & Fangshu Hui & Fangfei Li, 2023. "Stability of Differential Systems with Impulsive Effects," Mathematics, MDPI, vol. 11(20), pages 1-23, October.

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