复杂值记忆神经网络的事件触发的有限时间同步控制的非分解方法
Hui Lin1, Yanchao Shi1, Jun Guo2
1School of Science, Southwest Petroleum University, Chengdu, 610500 China.
Cognitive neurodynamics
|July 24, 2025
概括
这项研究使用事件触发控制实现了复杂值记忆神经网络 (CVMNNs) 的有限时间同步. 这种新的方法减少了通信负载,并防止了Zeno行为,确保了高效的网络同步.
科学领域:
- 神经网络的神经网络的神经网络
- 控制理论 控制理论
- 复杂的系统复杂的系统.
背景情况:
- 记忆神经网络 (MNN) 对于先进的计算至关重要.
- 复杂值的MNNs (CVMNNs) 与时间变化的延迟的同步带来了重大挑战.
- 现有的方法往往需要持续的通信,增加网络负载.
研究的目的:
- 调查CVMNN的有限时间同步与时间变化的延迟.
- 制定一个高效的事件触发控制策略,以减少通信负担.
- 确保强大且可实施的同步机制.
主要方法:
- 采用了整体分析方法,使用复杂的数值一-规范和符号函数.
- 引入了一个由事件触发的控制策略,具有特定的同步标准.
- 导出了对同步时间的直接估计和事件间隔的下限,以避免Zeno行为.
- 设计了一个自动触发的机制,以消除连续监控.
主要成果:
- 成功演示了在时间变化的延迟下,CVMNNs的有限时间同步.
- 事件触发控制显著减轻了通信压力.
- 通过在最小事件间隔上建立一个正下限来阻止Zeno行为.
- 拟议的自动触发机制提高了实际适用性.
结论:
- 开发的事件触发和自我触发的控制方法为CVMNN同步提供了有效的解决方案.
- 这种方法在计算上是高效的,并且对于现实世界的实现来说是实用的.
- 通过模拟和说明性示例来验证有效性.
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