Exponential stability and periodic solution for fuzzy BAM Neural networks with time varying delays

作     者：XIANG Hong-jun WANG Jin-hua Department of Mathematics, Xiangnan University, Chenzhou 423000, China 

作者机构：Department of Mathematics Xiangnan University Chenzhou 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2期

页      面：157-166页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：Supported by the National Natural Science Foundation of China (60574043) the Science Foundation of the Education Committee of Hunan Province (06C792 07C700) the Construction Program of Key Disciplines in Hunan Province,Aid Program for Science and Technology Innovative Research Team in Higher Educational Institutions of Hunan province 

主　　题：fuzzy BAM neural network periodic solution exponential stability linear matrix inequality(LMI) Lyapunov-Krasovskii functional 

摘      要：In this paper, a class of fuzzy BAM neural networks with time varying delays is discussed. By using the properties of M-matrix, Linear Matrix Inequality（LMI） approach and general Lyapunov-Krasovskii functional, some new sufficient conditions are derived to ensure the existence of periodic solutions and the global exponential stability of the fuzzy BAM neural networks with time varying delays. These results have important significance in the design of global exponential stable BAM networks with delays. Moreover, an example is given to illustrate that the conditions of the results in the paper are feasible.