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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.12 P.1912~1920

http://doi.org/10.1631/jzus.2007.A1912


Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks


Author(s):  ZHANG Jian-hai, ZHANG Sen-lin, LIU Mei-qin

Affiliation(s):  Department of System Science & Engineering, School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   zjuzjh@gmail.com, slzhang@zju.edu.cn, liumeiqin@zju.edu.cn

Key Words:  Standard neural network model (SNNM), Robust exponential stability, Recurrent neural networks (RNNs), Discrete-time, Time-delay system, Linear matrix inequality (LMI)


ZHANG Jian-hai, ZHANG Sen-lin, LIU Mei-qin. Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks[J]. Journal of Zhejiang University Science A, 2007, 8(12): 1912~1920.

@article{title="Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks",
author="ZHANG Jian-hai, ZHANG Sen-lin, LIU Mei-qin",
journal="Journal of Zhejiang University Science A",
volume="8",
number="12",
pages="1912~1920",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1912"
}

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%T Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks
%A ZHANG Jian-hai
%A ZHANG Sen-lin
%A LIU Mei-qin
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 12
%P 1912~1920
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1912

TY - JOUR
T1 - Robust exponential stability analysis of a larger class of discrete-time recurrent neural networks
A1 - ZHANG Jian-hai
A1 - ZHANG Sen-lin
A1 - LIU Mei-qin
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 12
SP - 1912
EP - 1920
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1912


Abstract: 
The robust exponential stability of a larger class of discrete-time recurrent neural networks (RNNs) is explored in this paper. A novel neural network model, named standard neural network model (SNNM), is introduced to provide a general framework for stability analysis of RNNs. Most of the existing RNNs can be transformed into SNNMs to be analyzed in a unified way. Applying Lyapunov stability theory method and S-Procedure technique, two useful criteria of robust exponential stability for the discrete-time SNNMs are derived. The conditions presented are formulated as linear matrix inequalities (LMIs) to be easily solved using existing efficient convex optimization techniques. An example is presented to demonstrate the transformation procedure and the effectiveness of the results.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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