Robust asymptotic stability for BAM neural networks with time-varying delays via LMI approach  

作　　者：LIU Jia ZONG Guang-deng ZHANG Yun-xi 

机构地区：[1]Research Institute of Automation, Qufu Normal University, Qufu 273165, China [2]School of Automation, Nanjing University of Science & Technology, Nanjing 210064, China

出　　处：《Applied Mathematics(A Journal of Chinese Universities)》2009年第3期282-290,共9页高校应用数学学报（英文版）（B辑）

基　　金：Supported by the National Natural Science Foundation of China (60674027;60875039);Specialized Research Fund for the Doctoral Program of Higher Education (20050446001);Scientific Research Foundation of Qufu Normal University

摘　　要：Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.Several novel stability conditions for BAM neural networks with time-varying delays are studied.Based on Lyapunov-Krasovskii functional combined with linear matrix inequality approach,the delay-dependent linear matrix inequality（LMI） conditions are established to guarantee robust asymptotic stability for given delayed BAM neural networks.These criteria can be easily verified by utilizing the recently developed algorithms for solving LMIs.A numerical example is provided to demonstrate the effectiveness and less conservatism of the main results.

关 键 词：robust asymptotic stability bidirectional associative memory （BAM） neural networks timevarying delays 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] TP13[自动化与计算机技术—控制科学与工程]