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Stability analysis of interval time-varying delayed neural networks including neutral time-delay and leakage delay

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  • Manivannan, R.
  • Samidurai, R.
  • Cao, Jinde
  • Alsaedi, Ahmed
  • Alsaadi, Fuad E.

Abstract

This paper addresses an improved stability criterion for an interval time-delayed neural networks (NNs) including neutral delay and leakage delay. By proposing a suitable Lyapunov–Krasovskii functionals (LKFs) together with the Auxiliary function-based integral inequality (AFBII) and reciprocally convex approach (RCC) approach. The major purpose of this research is put forward to the consideration of inequality techniques together with a suitable LKFs, and mixed with the Leibniz–Newton formula within the structure of linear matrix inequalities (LMIs). It is amazing that, the leakage delay has a disrupting impact on the stability behaviour of such system and they cannot be neglected. Finally, numerical examples have been demonstrated to showing feasibility and applicability of the developed technique. In addition, the developed stability criteria tested for feasibility of the benchmark problem to explore the real-world application in the sense of discrete time-delay and leakage delay as a process variable in the system model.

Suggested Citation

  • Manivannan, R. & Samidurai, R. & Cao, Jinde & Alsaedi, Ahmed & Alsaadi, Fuad E., 2018. "Stability analysis of interval time-varying delayed neural networks including neutral time-delay and leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 114(C), pages 433-445.
    • Handle: RePEc:eee:chsofr:v:114:y:2018:i:c:p:433-445
    DOI: 10.1016/j.chaos.2018.07.041
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    References listed on IDEAS

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    1. Cao, Jinde & Ho, Daniel W.C., 2005. "A general framework for global asymptotic stability analysis of delayed neural networks based on LMI approach," Chaos, Solitons & Fractals, Elsevier, vol. 24(5), pages 1317-1329.
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    4. Park, Ju H. & Kwon, O.M., 2009. "Global stability for neural networks of neutral-type with interval time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 41(3), pages 1174-1181.
    5. Li, Ruoxia & Cao, Jinde, 2016. "Stability analysis of reaction-diffusion uncertain memristive neural networks with time-varying delays and leakage term," Applied Mathematics and Computation, Elsevier, vol. 278(C), pages 54-69.
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    Cited by:

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    3. Sang, Hong & Zhao, Ying & Wang, Peng & Wang, Yuzhong & Yu, Shuanghe & Dimirovski, Georgi M., 2023. "Finite-time peak-to-peak analysis for switched generalized neural networks comprised of finite-time unstable subnetworks," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    4. Wang, Fen & Chen, Yuanlong, 2021. "Mean square exponential stability for stochastic memristor-based neural networks with leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Chang, Wenting & Sang, Hong & Guo, Liangdong & Wu, Libing & Dimirovski, Georgi M., 2024. "Integrated L∞ anti-disturbance synchronization control for switched neural networks with unknown delays," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    6. Zhang, Zhongjie & Yu, Tingting & Zhang, Xian, 2022. "Algebra criteria for global exponential stability of multiple time-varying delay Cohen–Grossberg neural networks," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    7. Duan, Lian & Liu, Jinzhi & Huang, Chuangxia & Wang, Zengyun, 2022. "Finite-/fixed-time anti-synchronization of neural networks with leakage delays under discontinuous disturbances," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).

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