JZUS - Journal of Zhejiang University SCIENCE

Journal of Zhejiang University SCIENCE A 2005 Vol.6 No.1 P.32-37

http://doi.org/10.1631/jzus.2005.A0032

LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks*

Author(s): Sen-lin Zhang, Mei-qin Liu
Affiliation(s): . School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Corresponding email(s): slzhang@mail.hz.zj.cn
Key Words: Standard neural network model (SNNM), Bidirectional associative memory (BAM) neural network, Linear matrix inequality (LMI), Linear differential inclusion (LDI), Global asymptotic stability

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ZHANG Sen-lin, LIU Mei-qin. LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks[J]. Journal of Zhejiang University Science A, 2005, 6(1): 32-37.

@article{title="LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks",
author="ZHANG Sen-lin, LIU Mei-qin",
journal="Journal of Zhejiang University Science A",
volume="6",
number="1",
pages="32-37",
year="2005",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2005.A0032"
}

%0 Journal Article
%T LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks
%A ZHANG Sen-lin
%A LIU Mei-qin
%J Journal of Zhejiang University SCIENCE A
%V 6
%N 1
%P 32-37
%@ 1673-565X
%D 2005
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2005.A0032

TY - JOUR
T1 - LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks
A1 - ZHANG Sen-lin
A1 - LIU Mei-qin
J0 - Journal of Zhejiang University Science A
VL - 6
IS - 1
SP - 32
EP - 37
%@ 1673-565X
Y1 - 2005
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2005.A0032

Abstract
Chinese Summary
Academic Network
Reviewer Comment

Abstract: Studies on the stability of the equilibrium points of continuous bidirectional associative memory (BAM) neural network have yielded many useful results. A novel neural network model called standard neural network model (SNNM) is advanced. By using state affine transformation, the BAM neural networks were converted to SNNMs. Some sufficient conditions for the global asymptotic stability of continuous BAM neural networks were derived from studies on the SNNMs’ stability. These conditions were formulated as easily verifiable linear matrix inequalities (LMIs), whose conservativeness is relatively low. The approach proposed extends the known stability results, and can also be applied to other forms of recurrent neural networks (RNNs).

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References

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