New sets of criteria for exponential L2-L stability of Takagi-Sugeno fuzzy systems combined with hopfield neural networks

Choon Ki Ahn, Moon Kyou Song

    Research output: Contribution to journalArticlepeer-review

    27 Citations (Scopus)

    Abstract

    In this paper, we propose new sets of criteria for exponential robust stability of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. The L2-L approach is applied to obtain new sets of stability criteria, under which T-S fuzzy Hopfield neural networks reduce the effect of external input to a prescribed level. These sets of criteria are presented based on the matrix norm and linear matrix inequality (LMI). The proposed sets of criteria also guarantee exponential stability for T-S fuzzy Hopfield neural networks without external input.

    Original languageEnglish
    Pages (from-to)2979-2986
    Number of pages8
    JournalInternational Journal of Innovative Computing, Information and Control
    Volume9
    Issue number7
    Publication statusPublished - 2013

    Keywords

    • Exponential L-L stability
    • Linear matrix inequality (LMI)
    • Matrix norm
    • Takagi-Sugeno (T-S) fuzzy hopfield neural network

    ASJC Scopus subject areas

    • Software
    • Theoretical Computer Science
    • Information Systems
    • Computational Theory and Mathematics

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