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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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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