Global Exponential Synchronization of Coupled Delayed Memristive Neural Networks with Reaction-Diffusion Terms via Distributed Pinning Controls

Zhenyuan Guo, Shiqin Wang, Jun Wang

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)

Abstract

This article presents new theoretical results on global exponential synchronization of nonlinear coupled delayed memristive neural networks with reaction-diffusion terms and Dirichlet boundary conditions. First, a state-dependent memristive neural network model is introduced in terms of coupled partial differential equations. Next, two control schemes are introduced: distributed state feedback pinning control and distributed impulsive pinning control. A salient feature of these two pinning control schemes is that only partial information on the neighbors of pinned nodes is needed. By utilizing the Lyapunov stability theorem and Divergence theorem, sufficient criteria are derived to ascertain the global exponential synchronization of coupled neural networks via the two pining control schemes. Finally, two illustrative examples are elaborated to substantiate the theoretical results and demonstrate the advantages and disadvantages of the two control schemes.

Original languageEnglish
Article number9040641
Pages (from-to)105-116
Number of pages12
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume32
Issue number1
DOIs
Publication statusPublished - Jan 2021
Externally publishedYes

Keywords

  • Distributed pinning controls
  • global exponential synchronization
  • memristive neural network
  • reaction-diffusion

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