自动触发合动态网络的静态固定同步控制.
Lingzhong Zhang1, Shengyuan Xu2
1School of Electrical Engineering and Automation, Changshu Institute of Technology, Changshu 215500, PR China.
概括
一种新的资源意识间歇性控制方法高效地同步合的神经网络. 这种自动触发机制将控制更新减少34.58%,简化了大型网络的计算.
科学领域:
- 控制理论 控制理论
- 网络同步 网络同步
- 计算神经科学是一种神经科学.
背景情况:
- 结合的神经网络需要强大的同步策略.
- 现有的间歇性控制方法在灵活性和效率方面存在局限性.
- 静态固定控制为网络同步提供了一个框架.
研究的目的:
- 提出基于资源意识触发的新静态固定间歇性控制策略.
- 开发一种自动触发机制 (STM) 以在多层网络中有效执行控制.
- 为了放松平均无周期间歇控制 (AIC) 的控制宽度的限制.
主要方法:
- 一个多层层次的分层网络结构,有钉定层和交互层.
- 使用AIC速率和辅助函数固定间歇同步的新定律.
- 一种自动触发机制 (STM) 用于在固定层和交互层中AIC执行.
- 拉普拉斯矩阵的分解用于大规模网络分析.
主要成果:
- 拟议的STM有效地启动静态固定冲动控制.
- 同步是通过楚亚的电路,振荡器和小世界网络实现的.
- 与定期事件触发计划相比,控制更新减少了34.58%.
- 层次分解可以降低大型网络的计算复杂性.
结论:
- 资源意识的静态固定间歇控制与STM对于合网络同步是有效的.
- 拟议的方法提高了控制效率,并减少了计算负载.
- 这种方法为网络控制问题提供了灵活而强大的工具.
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