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 journalArticle

    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)1-16
    Number of pages16
    JournalNeural Processing Letters
    DOIs
    Publication statusE-pub ahead of print - 21 Nov 2017

    Keywords

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

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