...
首页> 外文期刊>Neural Networks: The Official Journal of the International Neural Network Society >Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks
【24h】

Existence and global exponential stability of periodic solution for high-order discrete-time BAM neural networks

机译:高阶离散时间BAM神经网络周期解的存在性和全局指数稳定性

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

This paper concerns the existence and exponential stability of periodic solution for the high-order discrete-time bidirectional associative memory (BAM) neural networks with time-varying delays. First, we present the criteria for the existence of periodic solution based on the continuation theorem of coincidence degree theory and the Young's inequality, and then we give the criteria for the global exponential stability of periodic solution by using a non-Lyapunov method. After that, we give a numerical example that demonstrates the effectiveness of the theoretical results. The criteria presented in this paper are easy to verify. In addition, the proposed analysis method is easy to extend to other high-order neural networks.
机译:本文研究具有时变时滞的高阶离散时间双向联想记忆(BAM)神经网络周期解的存在性和指数稳定性。首先,基于重合度理论的连续性定理和杨氏不等式,给出了周期解存在性的判据,然后通过非李雅普诺夫方法给出了周期解的全局指数稳定性的判据。之后,我们给出一个数值示例,证明理论结果的有效性。本文提出的标准很容易验证。另外,所提出的分析方法易于扩展到其他高阶神经网络。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号