一个半循环函数用于采样数据同步的延迟神经网络,考虑到通信延迟
Yun-Hao An1, Xing-Chen Shangguan1, Hong-Zhang Wang1
1School of Automation, China University of Geosciences, Wuhan, 430074, China; Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, Wuhan, 430074, China; Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, Wuhan, 430074, China.
概括
本研究提出了一种使用采样数据控制的延迟神经网络 (DNN) 中主-奴隶同步的新方法. 该方法允许更长的采样间隔,减少通信和计算负载.
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.
- 网络化系统 网络化系统
背景情况:
- 延迟神经网络 (DNN) 对于模拟复杂的生物和人工系统至关重要.
- 在DNN中,主-奴隶同步对于协调信息处理至关重要,但由于延迟而具有挑战性.
- 现有的同步方法往往施加限制性条件,限制实际应用.
研究的目的:
- 开发一种新的采样数据控制器,以实现在DNN中实现主-奴隶同步,并有通信延迟.
- 为了减少同步标准中的保守主义,使更大的采样间隔成为可能.
- 降低网络DNN系统中的通信和计算负担.
主要方法:
- 构建一个新的半循环功能,以结合更多的系统信息和放松约束.
- 开发两个零值方程以协调系统信息并提高控制器设计灵活性.
- 使用拟议的技术,推导一个减少保守主义的同步标准.
主要成果:
- 对于具有通信延迟的延迟神经网络,成功地推导出了一个新的同步标准.
- 与现有方法相比,拟议的标准允许采样间隔显著更长.
- 通过三个数值示例验证了有效性和优越性.
结论:
- 开发的采样数据同步控制器有效地解决了DNN中的主-奴隶同步与通信延迟的问题.
- 拟议的方法为同步设计提供了更灵活,更少保守的方法.
- 这项研究有助于更有效,更实用的同步延迟神经网络的实施.
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