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Event-Based Pinning Strategy for Synchronizing Memristive Neural Networks With Delays

Qiang Jia*, Peipei Zhou, Xiaowen Bi, Shuiming Cai

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

This brief proposes a novel event-based pinning strategy for reaching synchronization of a typical class of delayed memristive neural network (MNN) under a master-slave framework. By initially injecting the pinning signal into the neuron with maximal synchrony error, the control is then switched between the neurons according to certain well-designed triggering condition. By incorporating the Lyapunov function method with some Halanay-type inequality, it is proved that such a design suffices to synchronize two MNNs with an exponential rate. It is worth to mention that the restriction of diffusive coupling for conventional pinning control schemes is unnecessary herein, and the gain used here keeps fixed which can thus avoid certain drawback of existing pinning strategies for synchronizing MNNs. A numerical example is finally given to demonstrate the feasibility and performance of the proposed design. © 2024 IEEE
Original languageEnglish
Pages (from-to)5019-5023
JournalIEEE Transactions on Circuits and Systems II: Express Briefs
Volume71
Issue number12
Online published29 Jul 2024
DOIs
Publication statusPublished - Dec 2024

Research Keywords

  • Circuits
  • complex network
  • Control systems
  • Delays
  • event-triggering
  • Halanay-type inequality
  • Main-secondary
  • memristive neural network
  • Multi-layer neural network
  • Neurons
  • Pinning control
  • Synchronization

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