Global synchronization of memristive neural networks subject to random disturbances via distributed pinning control

Zhenyuan Guo, Shaofu Yang, Jun Wang

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)

Abstract

This paper presents theoretical results on global exponential synchronization of multiple memristive neural networks in the presence of external noise by means of two types of distributed pinning control. The multiple memristive neural networks are coupled in a general structure via a nonlinear function, which consists of a linear diffusive term and a discontinuous sign term. A pinning impulsive control law is introduced in the coupled system to synchronize all neural networks. Sufficient conditions are derived for ascertaining global exponential synchronization in mean square. In addition, a pinning adaptive control law is developed to achieve global exponential synchronization in mean square. Both pinning control laws utilize only partial state information received from the neighborhood of the controlled neural network. Simulation results are presented to substantiate the theoretical results.

Original languageEnglish
Pages (from-to)67-79
Number of pages13
JournalNeural Networks
Volume84
DOIs
Publication statusPublished - 1 Dec 2016
Externally publishedYes

Keywords

  • Memristive neural networks
  • Pinning adaptive control
  • Pinning impulsive control
  • Random disturbance
  • Synchronization

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