| 引用本文: | 解永凯,童东兵,陈巧玉,周武能.基于自适应事件触发牵制控制的多时滞随机耦合神经网络簇同步[J].控制理论与应用,2023,40(2):275~282.[点击复制] |
| Xie Yong-kai,Tong Dong-bing,Chen Qiao-yu,Zhou Wu-neng.Cluster synchronization of multi-delayed stochastic coupled neural networks via adaptive event-triggered pinning control[J].Control Theory and Technology,2023,40(2):275~282.[点击复制] |
|
| 基于自适应事件触发牵制控制的多时滞随机耦合神经网络簇同步 |
| Cluster synchronization of multi-delayed stochastic coupled neural networks via adaptive event-triggered pinning control |
| 摘要点击 877 全文点击 299 投稿时间：2022-02-13 修订日期：2022-05-13 |
| 查看全文 查看/发表评论 下载PDF阅读器 |
| DOI编号 10.7641/CTA.2022.20109 |
| 2023,40(2):275-282 |
| 中文关键词 多时滞 事件触发 随机耦合神经网络 簇同步 |
| 英文关键词 multi-delay event-triggering stochastic coupled neural networks cluster synchronization |
| 基金项目 国家自然科学基金项目(61673257), 上海市自然科学基金项目(20ZR1422400), 中国博士后科学基金项目(2019M661322)资助. |
|
| 中文摘要 |
| 本文通过自适应事件触发牵制控制策略, 研究了多时滞的随机耦合神经网络在均方意义下以指数速率进
行簇同步的问题. 在耦合神经网络中, 同一簇中的节点只需与相应的孤立节点同步, 而对于不同簇中节点之间的同
步状态没有要求. 首先, 本文提出了一种事件触发牵制控制方法来解决耦合神经网络中节点数量众多、通讯复杂的
问题. 该方法不仅能减少耦合神经网络中控制器的数量, 还可以减少控制信号的传输次数、减轻网络传输压力. 然
后根据M矩阵方法, 建立了随机耦合神经网络均方指数稳定的充分条件. 同时, 利用自适应控制策略, 给出了反馈
增益的更新规律. 最后, 通过一个数值例子验证了所提出的自适应事件触发牵制控制策略的有效性和适用性. |
| 英文摘要 |
| In this paper, the cluster synchronization of multi-delayed stochastic coupled neural networks at exponential
rate in the sense of mean square is studied by the adaptive event-triggered pinning control strategy. In coupled neural
networks, nodes in the same cluster only need to synchronize with the corresponding isolated nodes, but there is no requirement for the synchronization state between nodes in different clusters. Firstly, an event-triggered pinning control method
is proposed to solve the problems of large number of nodes and complex communication in the coupled neural networks.
This method not only reduce the number of controllers in the coupled neural networks, but also reduce the transmission
times of control signals and the transmission pressure of the network. Then, according to the M-matrix method, a sufficient
condition for the mean square exponential stability of stochastic coupled neural networks is established. At the same time,
the update law of feedback gain is given by the adaptive control strategy. Finally, a numerical example is given to verify
the effectiveness and applicability of the proposed adaptive event-triggered pinning control strategy. |
|
|
|
|
|