分数阶时滞Cohen-Grossberg型BAM神经网络S-渐近ω-周期解  

Global S-asymptotic ω-periodic solutions for fractional Cohen-Grossberg BAM neural networks with delay

作　　者：蒋望东 章月红 刘伟[1] JIANG Wang-dong;ZHANG Yue-hong;LIU Wei(Department of Mathematics,Yuanpei College of Shaoxing University,Shaoxing 312000,China)

机构地区：[1]绍兴文理学院元培学院数学教研部,浙江绍兴312000

出　　处：《高校应用数学学报（A辑）》2020年第4期455-469,共15页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：浙江省高等教育教学改革研究项目(JG20160261);教育部产学合作协同育人项目(201801123017);绍兴市高等教育教学改革研究项目(SXSJG201833)。

摘　　要：主要研究分数阶时滞Cohen-Grossberg型BAM神经网络的有界性和周期性问题.利用分数阶微积分性质,借助于微分中值定理和Ascoli-Arzela定理,给出了判定系统解的有界性,S-渐近ω-周期和全局渐近ω-周期解的充分条件.最后通过数值模拟例子验证所得到理论结果的有效性.This paper mainly studies the boundedness and periodicity of fractional Cohen Grossberg type BAM neural network with delay.By using the properties of fractional calculus,differential mean value theorem and Ascoli-Arzela theorem,the sufficient conditions for determining the boundedness of system solution and global S-asymptoticωperiodic solution are given.Finally,the validity of the theoretical results is verified by numerical simulation examples.

关 键 词：分数阶 Cohen-Grossberg型BAM神经网络 有界性 S-渐近ω-周期 全局渐近ω-周期解 

分 类 号：O175.12[理学—数学]