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


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

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.

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%T LMI-based approach for global asymptotic stability analysis of continuous BAM neural networks
%A ZHANG Sen-lin
%A LIU Mei-qin
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%DOI 10.1631/jzus.2005.A0032

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
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SP - 32
EP - 37
%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2005.A0032

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).

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


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