• DocumentCode
    742228
  • Title

    Graph Theory-Based Approach for Stability Analysis of Stochastic Coupled Systems With Lévy Noise on Networks

  • Author

    Chunmei Zhang ; Wenxue Li ; Ke Wang

  • Author_Institution
    Dept. of Math., Harbin Inst. of Technol., Harbin, China
  • Volume
    26
  • Issue
    8
  • fYear
    2015
  • Firstpage
    1698
  • Lastpage
    1709
  • Abstract
    In this paper, a novel class of stochastic coupled systems with Lévy noise on networks (SCSLNNs) is presented. Both white noise and Lévy noise are considered in the networks. By exploiting graph theory and Lyapunov stability theory, criteria ensuring pth moment exponential stability and stability in probability of these SCSLNNs are established, respectively. These principles are closely related to the topology of the network and the perturbation intensity of white noise and Lévy noise. Moreover, to verify the theoretical results, stochastic coupled oscillators with Lévy noise on a network and stochastic Volterra predator-prey system with Lévy noise are performed. Finally, a numerical example about oscillators´ network is provided to illustrate the feasibility of our analytical results.
  • Keywords
    Lyapunov methods; asymptotic stability; graph theory; perturbation techniques; predator-prey systems; probability; stability criteria; stochastic systems; white noise; Lyapunov stability theory; SCSLNN; graph theory-based approach; network topology; perturbation intensity; probability; pth moment exponential stability criteria; stochastic Volterra predator-prey system; stochastic coupled oscillators; stochastic coupled system-with-Lévy noise on networks; white noise; Graph theory; Lyapunov methods; Numerical stability; Stability analysis; Stochastic processes; White noise; Lévy noise; L??vy noise; networks; stability; stochastic coupled systems;
  • fLanguage
    English
  • Journal_Title
    Neural Networks and Learning Systems, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    2162-237X
  • Type

    jour

  • DOI
    10.1109/TNNLS.2014.2352217
  • Filename
    6894589