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Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.7 P.689~696

http://doi.org/10.1631/jzus.2005.A0689


Stability analysis of discrete-time BAM neural networks based on standard neural network models


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

Affiliation(s):  School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

Corresponding email(s):   slzhang@mail.hz.zj.cn, liumeiqin@cee.zju.edu.cn

Key Words:  Standard neural network model (SNNM), Bidirectional associative memory (BAM), Linear matrix inequality (LMI), Stability, Generalized eigenvalue problem (GEVP)


ZHANG Sen-lin, LIU Mei-qin. Stability analysis of discrete-time BAM neural networks based on standard neural network models[J]. Journal of Zhejiang University Science A, 2005, 6(7): 689~696.

@article{title="Stability analysis of discrete-time BAM neural networks based on standard neural network models",
author="ZHANG Sen-lin, LIU Mei-qin",
journal="Journal of Zhejiang University Science A",
volume="6",
number="7",
pages="689~696",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0689"
}

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%T Stability analysis of discrete-time BAM neural networks based on standard neural network models
%A ZHANG Sen-lin
%A LIU Mei-qin
%J Journal of Zhejiang University SCIENCE A
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%P 689~696
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%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0689

TY - JOUR
T1 - Stability analysis of discrete-time BAM neural networks based on standard neural network models
A1 - ZHANG Sen-lin
A1 - LIU Mei-qin
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 7
SP - 689
EP - 696
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0689


Abstract: 
To facilitate stability analysis of discrete-time bidirectional associative memory (BAM) neural networks, they were converted into novel neural network models, termed standard neural network models (SNNMs), which interconnect linear dynamic systems and bounded static nonlinear operators. By combining a number of different Lyapunov functionals with S-procedure, some useful criteria of global asymptotic stability and global exponential stability of the equilibrium points of SNNMs were derived. These stability conditions were formulated as linear matrix inequalities (LMIs). So global stability of the discrete-time BAM neural networks could be analyzed by using the stability results of the SNNMs. Compared to the existing stability analysis methods, the proposed approach is easy to implement, less conservative, and is applicable to other recurrent neural networks.

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Reference

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[7] Liu, M.Q., Zhang, S.L., 2003. Stability analysis of a class of discrete-time recurrent neural networks: an LMI approach. Journal of Zhejiang University (Engineering Science), 37(1):19-23 (in Chinese).

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