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

Choon Ki Ahn, Moon Kyou Song

School of Electrical Engineering

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

26 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 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

@article{e9751c86738043a89825ae3f507e2f69,

title = "New sets of criteria for exponential L2-L∞ stability of Takagi-Sugeno fuzzy systems combined with hopfield neural networks",

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.",

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

author = "Ahn, {Choon Ki} and Song, {Moon Kyou}",

year = "2013",

language = "English",

volume = "9",

pages = "2979--2986",

journal = "International Journal of Innovative Computing, Information and Control",

issn = "1349-4198",

publisher = "IJICIC Editorial Office",

number = "7",

}

TY - JOUR

T1 - New sets of criteria for exponential L2-L∞ stability of Takagi-Sugeno fuzzy systems combined with hopfield neural networks

AU - Ahn, Choon Ki

AU - Song, Moon Kyou

PY - 2013

Y1 - 2013

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

UR - http://www.scopus.com/inward/record.url?scp=84880050245&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84880050245&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84880050245

SN - 1349-4198

VL - 9

SP - 2979

EP - 2986

JO - International Journal of Innovative Computing, Information and Control

JF - International Journal of Innovative Computing, Information and Control

IS - 7

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

Other files and links

Fingerprint

Cite this