Stability analysis of discrete-time BAM neural networks based on standard neural network models  

作　　者：张森林 刘妹琴 

机构地区：[1]School of Electrical Engineering Zhejiang University

出　　处：《Journal of Zhejiang University-Science A(Applied Physics & Engineering)》2005年第7期689-696,共8页浙江大学学报（英文版）A辑（应用物理与工程）

基　　金：Project (No. 60074008) supported by the National Natural Science Foundation of China

摘　　要：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.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.

关 键 词：Standard neural network model （SNNM） Bidirectional associative memory （BAM） Linear matrix inequality （LMI） STABILITY Generalized eigenvalue problem （GEVP） 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]