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Pinning distributed synchronization of stochastic dynamical networks: A mixed optimization approach

  • Yang Tang*
  • , Huijun Gao
  • , Jianquan Lu
  • , Jürgen Kurths
  • *Corresponding author for this work
  • Potsdam Institute for Climate Impact Research
  • Humboldt University of Berlin
  • King Abdulaziz University
  • Southeast University, Nanjing
  • University of Aberdeen

Research output: Contribution to journalArticlepeer-review

Abstract

This paper is concerned with the problem of pinning synchronization of nonlinear dynamical networks with multiple stochastic disturbances. Two kinds of pinning schemes are considered: 1) pinned nodes are fixed along the time evolution and 2) pinned nodes are switched from time to time according to a set of Bernoulli stochastic variables. Using Lyapunov function methods and stochastic analysis techniques, several easily verifiable criteria are derived for the problem of pinning distributed synchronization. For the case of fixed pinned nodes, a novel mixed optimization method is developed to select the pinned nodes and find feasible solutions, which is composed of a traditional convex optimization method and a constraint optimization evolutionary algorithm. For the case of switching pinning scheme, upper bounds of the convergence rate and the mean control gain are obtained theoretically. Simulation examples are provided to show the advantages of our proposed optimization method over previous ones and verify the effectiveness of the obtained results.

Original languageEnglish
Article number6704289
Pages (from-to)1804-1815
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number10
DOIs
StatePublished - 1 Oct 2014

Keywords

  • Complex networks
  • evolutionary algorithms (EAs)
  • multiagent systems
  • neural networks
  • stochastic disturbances
  • synchronization.

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