Pinning Distributed Synchronization of Stochastic Dynamical Networks: A Mixed Optimization Approach

Yang Tang, Huijun Gao, Jianquan Lu, Jurgen Kurths

Research output: Contribution to journalArticle

98 Citations (Scopus)

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 verifi-
able 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
Pages (from-to)1804 - 1815
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume25
Issue number10
Early online date9 Jan 2014
DOIs
Publication statusPublished - Oct 2014

Keywords

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

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