Global exponential stability analysis of cellular neural networks with multiple time delays  

作　　者：Zhanshan WANG Huaguang ZHANG 

机构地区：[1]School of Information Science and Engineering, Northeastern University, Shenyang Liaoning 110004, China [2]Department of Information Engineering, Shenyang Ligong University, Shenyang Liaoning 110168, China

出　　处：《控制理论与应用（英文版）》2007年第2期105-112,共8页

基　　金：the National Natural Science Foundation of China (No.60274017, 60325311).

摘　　要：Global exponential stability problems are investigated for cellular neural networks （CNN） with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.Global exponential stability problems are investigated for cellular neural networks （CNN） with multiple time-varying delays. Several new criteria in linear matrix inequality form or in algebraic form are presented to ascertain the uniqueness and global exponential stability of the equilibrium point for CNN with multiple time-varying delays and with constant time delays. The proposed method has the advantage of considering the difference of neuronal excitatory and inhibitory effects, which is also computationally efficient as it can be solved numerically using the recently developed interior-point algorithm or be checked using simple algebraic calculation. In addition, the proposed results generalize and improve upon some previous works. Two numerical examples are used to show the effectiveness of the obtained results.

关 键 词：Cellular neural networks Multiple time-varying delays Exponential stability Linear matrix inequality （LMI） Lyapunov-Krasovskii functional 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]