Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

Tengda Wei, Yangfan Wang (Lead / Corresponding author), Linshan Wang (Lead / Corresponding author)

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.

Original languageEnglish
Pages (from-to)979–994
Number of pages16
JournalNeural Processing Letters
Volume48
Early online date21 Nov 2017
DOIs
Publication statusPublished - 2018

Keywords

  • Markovian jumping parameter
  • Reaction–diffusion
  • Stochastic delayed neural network
  • Synchronization
  • Wiener process

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