高阶脉冲变时滞BAM神经网络的周期解  

Periodic Solution of Impulsive High-order BAM Neural Networks with Time-varying Delays

作　　者：吴春雪[1] 

机构地区：[1]烟台大学数学与信息科学学院,山东烟台264005

出　　处：《烟台大学学报（自然科学与工程版）》2015年第3期157-161,共5页Journal of Yantai University(Natural Science and Engineering Edition)

摘　　要：神经网络的诸多功能主要体现在其动力学特征中,而周期解问题则是其动力学行为研究中很重要的一部分.许多情况下,考虑神经网络的脉冲效应是必要而具有实际价值的.本文利用重合度理论中的Gaines-Mawhin延拓定理和微分不等式技巧,研究一类具脉冲干扰的高阶BAM神经网络模型的周期解问题,在要求激活函数有界的前提下,得到其周期解存在的充分条件.The information processing function of neural networks mostly reflects in its dynamic characteristics. The periodic solution problem is one of the most important parts in the research of neural network dynamic actions in many cases. It is necessary and practically valuable to consider the impulse effect of neural networks. In this pa- per, by using the continuation theorem of Mawhin＇ s coincidence degree theory and differential inequalities, suffi- cient conditions are obtained for the existence of periodic solution of higher-order BAM neural networks with varia- ble delays and impulses under the requirement of boundedness of involved functions.

关 键 词：重合度理论 BAM神经网络 周期解 脉冲 

分 类 号：O175[理学—数学]