结合神经网络与恒定时间延迟的同步使用采样数据信息
IEEE transactions on cybernetics
|October 12, 2023
概括
本研究介绍了使用采样数据对联神经网络 (CNN) 的同步控制方法. 拟议的方法确保了同步,减少了保守主义和通信负载.
科学领域:
- 控制系统工程 控制系统工程
- 计算神经科学是一种神经科学.
- 网络科学 网络科学
背景情况:
- 结合神经网络 (CNN) 是神经科学和控制系统的基本模型.
- 由于固有的系统动态,在CNN中实现时间延迟的同步是具有挑战性的.
- 现有的方法通常需要连续的数据,这对于许多应用来说是不切实际的.
研究的目的:
- 开发一种新的同步控制方法,用于使用采样数据信息的常时延迟的CNN.
- 为了减少同步条件的保守性,并最大限度地降低通信能量负载.
- 为了确定有效同步的最大允许的采样间隔.
主要方法:
- 基于来自邻近节点的采样数据信息设计了一个分布式控制协议.
- 使用了利亚普诺夫的功能不等式和高级积分不等式 (帕克的,改进的自由重量矩阵).
- 制定了一个优化问题,以确定最大的采样间隔.
- 实施了定期抽样控制技术.
主要成果:
- 为了在CNN中实现与时间延迟不变的同步,在减少保守主义的情况下获得了足够的条件.
- 成功确定了最大的采样间隔.
- 非周期性采样控制技术有效降低了通信能源负载.
- 数字模拟验证了拟议方法实现同步的能力.
结论:
- 拟议的采样数据控制方法有效地实现了CNN与恒定时间延迟的同步.
- 与现有方法相比,该方法提供了较低的保守主义和较低的通信成本.
- 这些发现为设计用于延迟神经网络的同步控制器提供了实际框架.
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