时标上Leakage项变时滞BAM神经网络系统的概周期解

高瑾; 林园; 王其如

doi:10.13471/j.cnki.acta.snus.2019.06.21.2019A048

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时标上Leakage项变时滞BAM神经网络系统的概周期解

研究论文 | 更新时间：2023-11-01

- 时标上Leakage项变时滞BAM神经网络系统的概周期解
- Almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales
- 中山大学学报(自然科学版) 2020年59卷第5期页码：29-39
- 作者机构：
  
  1.深圳信息职业技术学院计算机学院,广东深圳 518172
  2.深圳信息职业技术学院公共课教学部, 广东深圳 518172
  3.中山大学数学学院，广东广州 510275
- 作者简介：
  
  高瑾（1985年生），女；研究方向：泛函微分方程；E-mail:306996140@qq.com
  林园（1987年生），男；研究方向：泛函微分方程；E-mail:229617098@qq.com
- 基金信息：
  
  国家自然科学基金(11671406);深圳信息职业技术学院校级科研培育项目(QN201703)
- DOI：10.13471/j.cnki.acta.snus.2019.06.21.2019A048
  中图分类号： O193
- 纸质出版日期：2020-09-25，
  
  收稿日期：2019-06-21，
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高瑾,林园,王其如.时标上Leakage项变时滞BAM神经网络系统的概周期解[J].中山大学学报(自然科学版),2020,59(05):29-39.

GAO Jin,LIN Yuan,WANG Qiru.Almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):29-39.
高瑾,林园,王其如.时标上Leakage项变时滞BAM神经网络系统的概周期解[J].中山大学学报(自然科学版),2020,59(05):29-39. DOI： 10.13471/j.cnki.acta.snus.2019.06.21.2019A048.

GAO Jin,LIN Yuan,WANG Qiru.Almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2020,59(05):29-39. DOI： 10.13471/j.cnki.acta.snus.2019.06.21.2019A048.

摘要

研究了时标上的Leakage项变时滞双向联想记忆（BAM）神经网络系统，给出了其概周期解存在性、唯一性和全局指数稳定性的充分条件。另外还给出了一个例子和数值模拟来说明得到结果的正确性。

Abstract

The almost-periodic solutions for BAM neural networks with time-varying delays in leakage terms on time scales are concerned. Some sufficient conditions about the existence

uniqueness and global exponential stability of almost-periodic solutions are given. An example and numerical simulations are established to illustrate the feasibility and effectiveness of the results.

关键词

双向联想记忆神经网络时标概周期解全局指数稳定性

Keywords

BAM neural networkstime scalesalmost-periodic solutionsglobal exponential stability

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