Distributed synchronization of coupled neural networks via randomly occurring control

Yang Tang, Wai Keung Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

184 Citations (Scopus)

Abstract

In this paper, we study the distributed synchronization and pinning distributed synchronization of stochastic coupled neural networks via randomly occurring control. Two Bernoulli stochastic variables are used to describe the occurrences of distributed adaptive control and updating law according to certain probabilities. Both distributed adaptive control and updating law for each vertex in a network depend on state information on each vertex's neighborhood. By constructing appropriate Lyapunov functions and employing stochastic analysis techniques, we prove that the distributed synchronization and the distributed pinning synchronization of stochastic complex networks can be achieved in mean square. Additionally, randomly occurring distributed control is compared with periodically intermittent control. It is revealed that, although randomly occurring control is an intermediate method among the three types of control in terms of control costs and convergence rates, it has fewer restrictions to implement and can be more easily applied in practice than periodically intermittent control.
Original languageEnglish
Pages (from-to)435-447
Number of pages13
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume24
Issue number3
DOIs
Publication statusPublished - 8 Oct 2013

Keywords

  • Bernoulli stochastic variables
  • Complex dynamical networks
  • Pinning control
  • Randomly occurring control
  • Stochastic disturbances

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

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