• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>Proceedings of the Ninth International Conference on Machine Learning and Cybernetics >Robust stability of discrete-time stochastic BAM neural networks with Markovian jumping parameters and time-varying delays
【24h】

Robust stability of discrete-time stochastic BAM neural networks with Markovian jumping parameters and time-varying delays

机译：具有马尔可夫跳跃参数和时变时滞的离散时间随机BAM神经网络的鲁棒稳定性

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

This paper investigates the problem of robust stability for a class of uncertain discrete-time stochastic bidirectional associative memory(BAM) neural networks with Markovian jumping parameters and time-varying delays. By employing the Lyapunov functional we can get novel robust stability conditions in terms of linear matrix inequality (LMI), which can be easily solved by MATLAB LMI toolbox. Furthermore, we will introduce into some free weighting matrices in order to lead to much less conservative results. At last, one numerical example is given to illustrate the effectiveness of the proposed results.
机译：研究了一类具有马尔可夫跳跃参数和时变时滞的不确定离散时间随机双向联想记忆（BAM）神经网络的鲁棒稳定性问题。通过使用Lyapunov函数，我们可以根据线性矩阵不等式（LMI）获得新颖的鲁棒稳定性条件，可以通过MATLAB LMI工具箱轻松解决该条件。此外，我们将引入一些自由加权矩阵，以减少保守的结果。最后，通过一个数值例子说明了所提出结果的有效性。

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