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Global robust stability of interval neural networks with multiple time-varying delays

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  • Song, Qiankun
  • Cao, Jinde

Abstract

In this paper, the global robust stability is investigated for interval neural networks with multiple time-varying delays. The neural network contains time-invariant uncertain parameters whose values are unknown but bounded in given compact sets. Without assuming both the boundedness on the activation functions and the differentiability on the time-varying delays, a new sufficient condition is presented to ensure the existence, uniqueness, and global robust stability of equilibria for interval neural networks with multiple time-varying delays based on the Lyapunov–Razumikhin technique as well as matrix inequality analysis. Several previous results are improved and generalized, and an example is given to show the effectiveness of the obtained results.

Suggested Citation

  • Song, Qiankun & Cao, Jinde, 2007. "Global robust stability of interval neural networks with multiple time-varying delays," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 74(1), pages 38-46.
    • Handle: RePEc:eee:matcom:v:74:y:2007:i:1:p:38-46
    DOI: 10.1016/j.matcom.2006.06.030
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    Cited by:

    1. Zhu, Song & Shen, Yi & Chen, Guici, 2012. "Noise suppress exponential growth for hybrid Hopfield neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 86(C), pages 10-20.
    2. Samidurai, R. & Sriraman, R., 2019. "Robust dissipativity analysis for uncertain neural networks with additive time-varying delays and general activation functions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 201-216.
    3. Liu, Leipo & Han, Zhengzhi & Cai, Xiushan & Huang, Jun, 2010. "Robust stabilization of linear differential inclusion system with time delay," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 80(5), pages 951-958.

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