分数阶时滞惯性BAM神经网络全局Mittag-Leffler同步稳定  

Global Mittag-Leffer synchronization of fractional-order inertial BAM neural networks with time delays

作　　者：章月红 李志英 蒋望东 ZHANG Yue-hong;LI Zhi-ying;JIANG Wang-dong(Shaoxing University Yuanpei College,Shaoxing 312000,China)

机构地区：[1]绍兴文理学院元培学院公共基础教育分院,浙江绍兴312000

出　　处：《高校应用数学学报（A辑）》2023年第2期190-202,共13页Applied Mathematics A Journal of Chinese Universities(Ser.A)

基　　金：浙江省教育厅一般科研项目(Y202145903);教育部产学合作协同育人项目(220603284143545,202102034006,202102283047);绍兴文理学院校级科研项目(2020LG1009);绍兴文理学院元培学院院级科研项目(KY2020C01,KY2021C04)。

摘　　要：该文研究一类分数阶时滞惯性BAM神经网络全局Mittag-Leffler同步稳定问题.引入变量替换,将含有二个不同阶分数阶导数的分数阶惯性时滞BAM神经网络模型简化为只含一个同阶分数阶导数的分数阶神经网络模型,利用Riemann-Liouville分数阶微积分性质,给出系统稳定性判定的两个不同的充分条件,通过两个数值模拟例子验证所得理论结果的正确性,同时说明对于给定的两个判定条件各有所长,可以根据系统参数设定情况,合理选取适合的判定定理.The global Mittag-Leffer synchronization of fractional-order inertial BAM neural networks with time delays is studied in this paper.By variable substitution,the fractional-order inertial neural networks with two diffeerent fractional-order derivatives are transformed to neural networks with only one fractional-order derivative.Using the Riemann-Liouville fractional calculus properties,two diffeerent suffcient conditions for the stability of the system are given.The correctness of the results is verified by two simulated examples.It shows that the su±cient conditions have their own advantages,according to the setting of system parameters.

关 键 词：分数阶 惯性BAM神经网络 变量替换 全局Mittag-Leffler同步稳定 

分 类 号：O175[理学—数学] TP183[理学—基础数学]