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Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays

Author

Listed:
  • Jiao, Shiyu
  • Shen, Hao
  • Wei, Yunliang
  • Huang, Xia
  • Wang, Zhen

Abstract

This work investigates the dissipativity and stability analysis problems for Markov jump generalized neural networks subject to time-varying interval delays. The aim of the present study is to determine whether the new dissipativity and stability criteria with less conservatism could be established for Markov jump generalized delayed neural networks or not. In this connection, a more general dissipative property index inequality is firstly introduced. On basis of fully considering mode-dependent matrices in the Lyapunov–Krasovskii functional, some advantageous negative terms ignored in some existing works are taken into account. By employing some novel integral inequalities and stochastic analysis theory, some available less conservative criteria are developed. Three compared examples are finally shown to explain the reduced conservatism and superiority of the presented criteria for Markov jump generalized delayed neural networks.

Suggested Citation

  • Jiao, Shiyu & Shen, Hao & Wei, Yunliang & Huang, Xia & Wang, Zhen, 2018. "Further results on dissipativity and stability analysis of Markov jump generalized neural networks with time-varying interval delays," Applied Mathematics and Computation, Elsevier, vol. 336(C), pages 338-350.
    • Handle: RePEc:eee:apmaco:v:336:y:2018:i:c:p:338-350
    DOI: 10.1016/j.amc.2018.05.013
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    References listed on IDEAS

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