具有混合时变时滞和脉冲扰动的区间广义BAM神经网络的分段伪概周期解

IF 1.8 3区 数学 Q1 MATHEMATICS
Yanshou Dong, Junfang Zhao, Xu Miao, Ming Kang
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引用次数: 0

摘要

研究了一类具有混合时变时滞和脉冲扰动的区间广义BAM神经网络的分段伪概周期解。采用线性微分方程的指数二分法和收缩映射的不动点理论。给出了具有混合时变时滞和脉冲扰动的区间广义BAM神经网络的分段伪概周期解存在的充分条件。采用微分不等式技术和数学归纳法,讨论了具有混合时变时滞和脉冲扰动的区间广义BAM神经网络的分段伪概周期解的全局指数稳定性。最后通过一个算例说明了所得结果的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Piecewise pseudo almost periodic solutions of interval general BAM neural networks with mixed time-varying delays and impulsive perturbations
This paper is concerned with piecewise pseudo almost periodic solutions of a class of interval general BAM neural networks with mixed time-varying delays and impulsive perturbations. By adopting the exponential dichotomy of linear differential equations and the fixed point theory of contraction mapping. The sufficient conditions for the existence of piecewise pseudo almost periodic solutions of the interval general BAM neural networks with mixed time-varying delays and impulsive perturbations are obtained. By adopting differential inequality techniques and mathematical methods of induction, the global exponential stability for the piecewise pseudo almost periodic solutions of the interval general BAM neural networks with mixed time-varying delays and impulsive perturbations is discussed. An example is given to illustrate the effectiveness of the results obtained in the paper.
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来源期刊
AIMS Mathematics
AIMS Mathematics Mathematics-General Mathematics
CiteScore
3.40
自引率
13.60%
发文量
769
审稿时长
90 days
期刊介绍: AIMS Mathematics is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in all fields of mathematics. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports.
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