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


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.

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T1 - Stability analysis of discrete-time BAM neural networks based on standard neural network models
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A1 - LIU Mei-qin
J0 - Journal of Zhejiang University Science A
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SP - 689
EP - 696
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DOI - 10.1631/jzus.2005.A0689

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.

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


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