分数阶变时滞Cohen-Grossberg型BAM神经网络全局Mittag-Leffler稳定  

Global Mittag-Leffler Stability of Fractional-order Cohen-Grossberg BAM Neural Networks with Time-varying Delays

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

机构地区：[1]绍兴文理学院元培学院

出　　处：《数学的实践与认识》2019年第20期303-314,共12页Mathematics in Practice and Theory

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

摘　　要：主要研究分数阶变时滞Cohen-Grossberg型BAM神经网络,利用分数阶微积分有关性质,定义Mittag-leffler函数和对时间区间的有效划分,借助微分中值定理和一些分析技巧,给出了判定其系统解全局Mittag-Leffler稳定性充分条件.最后,给出数值例子以验证理论结果的有效性.This paper mainly studies fractional-order Cohen-Grossberg BAM neural networks with time-varying delays.Using properties of fractional-order calculus,definition of the Mittag-leffler function,effective division of time intervals,differential mean value theorem and some analytical techniques,sufficient conditions is given to ensure global Mittag-Leffler stability of such fractional-order neural networks.Finally,numerical examples are given to verify the validity of the theoretical results.

关 键 词：分数阶 Cohen-Grossberg型BAM神经网络 微分中值定理 全局Mittag-Leffler稳定 

分 类 号：G63[文化科学—教育学]