时标上Leakage项变时滞BAM神经网络系统的概周期解  

Almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales

作　　者：高瑾[1] 林园[2] 王其如[3] GAO Jin;LIN Yuan;WANG Qiru(School of Computer Sciences,Shenzhen Institute of Information Technology,Shenzhen 518172,China;Department of Public Courses,Shenzhen Institute of Information Technology,Shenzhen 518172,China;School of Mathematics,Sun Yat-sen University,Guangzhou 510275,China)

机构地区：[1]深圳信息职业技术学院计算机学院,广东深圳518172 [2]深圳信息职业技术学院公共课教学部,广东深圳518172 [3]中山大学数学学院,广东广州510275

出　　处：《中山大学学报（自然科学版）》2020年第5期29-39,共11页Acta Scientiarum Naturalium Universitatis Sunyatseni

基　　金：国家自然科学基金(11671406);深圳信息职业技术学院校级科研培育项目(QN201703)。

摘　　要：研究了时标上的Leakage项变时滞双向联想记忆(BAM)神经网络系统,给出了其概周期解存在性、唯一性和全局指数稳定性的充分条件。另外还给出了一个例子和数值模拟来说明得到结果的正确性。The almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales are concerned.Some sufficient conditions about the existence,uniqueness and global exponential stability of almost-periodic solutions are given.An example and numerical simulations are established to illustrate the feasibility and effectiveness of the results.

关 键 词：双向联想记忆神经网络 时标 概周期解 全局指数稳定性 

分 类 号：O193[理学—数学]