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

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

作者机构：Research Institute of Automation Qufu Normal University Qufu 273165 China School of Automation Nanjing University of Science & Technology Nanjing 210064 China 

出 版 物：《Applied Mathematics(A Journal of Chinese Universities)》 (高校应用数学学报（英文版）（B辑）)

年 卷 期：2009年第24卷第3期

页      面：282-290页

核心收录：

学科分类：0711[理学-系统科学] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 081101[工学-控制理论与控制工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 071102[理学-系统分析与集成] 081103[工学-系统工程] 

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 

主　　题：robust asymptotic stability bidirectional associative memory (BAM) neural networks timevarying delays linear matrix inequality(LMI) Lyapunov-Krasovskii functional 

摘      要：Several novel stability conditions for BAM neural networks with time-varying delays are *** 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 *** 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.