延迟合神经网络与延迟补偿冲动控制的指数同步
Song Ling1, Hongmei Shi1, Huanqing Wang2
1School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing 100044, China.
ISA transactions
|November 17, 2023
概括
本研究引入了对合神经网络 (CNN) 的新型延迟补偿冲动控制,通过利用冲动延迟来稳定系统,实现全球指数同步 (GES).
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.
- 系统工程 系统工程
背景情况:
- 指数级同步对于合神经网络 (CNN) 来说至关重要.
- 现有的方法与延迟系统和破坏稳定的冲动控制作斗争.
- 冲动延迟往往会在网络同步方面带来挑战,而不是提供解决方案.
研究的目的:
- 为延迟的CNN开发一种新的延迟补偿冲动控制策略.
- 为实现全球指数同步 (GES) 建立足够的标准.
- 通过建设性地利用冲动延迟来克服现有方法的局限性.
主要方法:
- 拉祖米金式不平等的发展,适应破坏稳定的延迟冲动收益.
- 引入延迟补偿概念,强调系统稳定性中的两个关键作用.
- 整合冲动延迟以抵消由破坏稳定的收益引起的不稳定动态.
主要成果:
- 在延迟的CNN中,GES的足够标准是使用新的不平等和控制思想来得出的.
- 冲动延迟被有效地利用,以弥补瞬间不稳定的冲动动态.
- 在系统和冲动延迟之间放松约束,将冲动间隔与系统延迟脱.
结论:
- 建议的延迟补偿冲动控制方法提高了稳定性和同步性能.
- 这种新方法与现有方法相比显示出更高的性能,并通过实际应用得到验证.
- 这项工作为同步复杂神经网络提供了更灵活,更有效的框架.
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