马尔科夫跳跃神经网络通过异步输出反控制与通信约束的同步
概括
本研究使用事件触发通信模型解决了离散的马尔科夫跳跃神经网络同步问题. 新的方法确保了可靠的同步,尽管有通信限制和异步现象.
科学领域:
- 控制系统工程 控制系统工程
- 网络化系统 网络化系统
- 计算神经科学是一种神经科学.
背景情况:
- 同步对于像马尔科夫跳跃神经网络 (MJNNs) 这样的网络系统至关重要.
- 现有的方法往往忽视了实际的通信约束,例如事件触发的传输,量子化和异步现象.
- 节约通信资源,同时保持系统性能是一个重大挑战.
研究的目的:
- 为了研究离散马尔科夫跳跃神经网络 (MJNNs) 的同步问题.
- 提出一种包含事件触发传输,对数量化和异步现象的通用通信模型.
- 为MJNN开发异步输出反控制器,这些控制器可能无法提供状态信息.
主要方法:
- 一种具有事件触发传输,对数量化和异步现象的通用通信模型.
- 一个使用对角矩阵值参数的通用事件触发协议.
- 模式不匹配的隐藏马尔科夫模型 (HMM),以及异步输出反控制器的新解策略.
- 利亚普诺夫技术和线性矩阵不等式 (LMI) 来推导同步条件.
主要成果:
- 使用LMI和Lyapunov方法建立了MJNN消散同步的足够条件.
- 开发了一个更一般的事件触发协议,减少了保守主义.
- 一种隐藏的马尔科夫模型 (HMM) 方法有效地处理了由于时间滞后和数据包丢失的模式不匹配.
- 设计了异步输出反控制器,即使节点状态信息不可用.
结论:
- 提出的方法确保在实际通信约束下对离散马尔科夫跳跃神经网络进行有效和消散同步.
- 开发的通信模型和控制策略提供了较低的保守性和计算成本.
- 数字示例验证了拟议的同步方法的有效性.
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