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

• Published in: Journal of Zhejiang University. A. Science Vol. 6; no. 1; pp. 32 - 37
• Main Author: 张森林 刘妹琴
• Format: Journal Article
• Language: English
• Published: School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China 01.08.2005
• Subjects: 人工神经网络; 存储器; 标准化; 记忆模式; Global asymptotic stability; Bidirectional associative memory (BAM) neural network; Linear differential inclusion (LDI); Standard neural network model (SNNM); Linear matrix inequality (LMI)
• ISSN: 1673-565X; 1862-1775
• DOI: 10.1631/BF02842474

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).