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

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

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	2979-2986
Number of pages	8
Journal	International Journal of Innovative Computing, Information and Control
Volume	9
Issue number	7
Publication status	Published - 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

Cite this

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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.",

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AU - Song, Moon Kyou

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N2 - 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.

AB - 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.

KW - Exponential L-L stability

KW - Linear matrix inequality (LMI)

KW - Matrix norm

KW - Takagi-Sugeno (T-S) fuzzy hopfield neural network

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JF - International Journal of Innovative Computing, Information and Control

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ER -

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

Abstract

Keywords

ASJC Scopus subject areas

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