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Updated: Sep 17, 2025

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随机马科夫反应-扩散神经网络的异步边界稳定与模式依赖的延迟
概括
这项研究引入了对具有模式依赖延迟 (MDD) 的随机反应扩散神经网络的新型异步边界控制. 该方法增强了稳定性和同步性,为复杂系统提供了实际的解决方案.
科学领域:
- 控制理论 控制理论
- 计算神经科学是一种神经科学.
- 随机系统 随机系统 随机系统
背景情况:
- 反应-扩散神经网络对于建模时空动态至关重要.
- 由于环境限制和实施成本,需要异步控制.
- 模式依赖延迟 (MDD) 在系统分析和控制中带来了显著的复杂性.
研究的目的:
- 用MDD解决随机马可维反应-扩散神经网络的异步控制问题.
- 为纽曼和迪里克莱特边界条件开发新的异步边界控制 (BC) 策略.
- 将控制方法扩展到领导跟随者同步问题.
主要方法:
- 整合了一个隐藏的马尔科夫模型来管理模式异步.
- 整体异步边界控制器的开发.
- 为MDDs量身定制的指数稳定性标准的导出.
- 介绍一种新的异步BC合成方法.
主要成果:
- 介绍了一种使用MDD的随机反应扩散神经网络异步边界控制的新方法.
- 导出了MDDs特有的指数稳定性标准.
- 拟议的控制器被验证为诺伊曼和迪里克莱特边界条件.
- 证明了成功扩展到领袖-追随者同步.
结论:
- 开发的异步边界控制方案有效地管理复杂神经网络模型中的模式异步.
- 该方法提供了一种实用且优越的解决方案,用于增强MDD系统中的稳定性和同步性.
- 数字示例证实了拟议的控制设计的有效性和实用性.
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