首页    期刊浏览 2024年07月08日 星期一
登录注册

文章基本信息

  • 标题:Outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches
  • 本地全文:下载
  • 作者:Weike Cheng ; Ailong Wu ; Jin-E Zhang
  • 期刊名称:Advances in Difference Equations
  • 印刷版ISSN:1687-1839
  • 电子版ISSN:1687-1847
  • 出版年度:2019
  • 卷号:2019
  • 期号:1
  • 页码:1-31
  • DOI:10.1186/s13662-019-2320-0
  • 出版社:Hindawi Publishing Corporation
  • 摘要:This paper is committed to investigating outer-synchronization of fractional-order neural networks with deviating argument via centralized and decentralized data-sampling approaches. Considering the low cost and high reliability of data-sampling control, we adopt two categories of control strategies with principles of centralized and decentralized data-sampling to synchronize fractional-order neural networks with deviating argument. Several sufficient criteria are proposed to realize outer-synchronization by data-sampling control design in two complex coupled networks. It is noteworthy that, based on centralized and decentralized data-sampling methods, the synchronization theory of fractional systems and differential equation with deviating argument, the sampling time points are very well selected in control systems. An example is performed to illustrate the advantage of the presented theoretical analysis and results.
  • 关键词:Fractional-order systems;Deviating argument;Outer-synchronization;Centralized and decentralized data-sampling principles
国家哲学社会科学文献中心版权所有