在水库系统中收缩和同步.
Adrian S Wong1, Robert S Martin2, Daniel Q Eckhardt3
1Edwards AFB, Air Force Research Laboratory, Jacobs, Technology Inc. and In-Space Propulsion Branch, California 93524, USA.
这项研究揭示了如何通过控制全球收缩来确保泄漏的连续时间库的普遍同步. 它为构建连接矩阵提供了指导方针,并讨论了这些系统的通用近似属性.
科学领域:
- 复杂的系统复杂的系统.
- 机器学习 机器学习
- 动态系统 动态系统
背景情况:
- 储计算提供了一种数据驱动的方法来建模动态系统.
- 连续时间储备被探索为他们的同步和近似能力.
研究的目的:
- 为了研究在泄漏的连续时间储库中普遍同步的条件.
- 为构建有效的连接矩阵提供准则.
- 分析离散和连续时间储库的普遍近似性质.
主要方法:
- 使用连接矩阵的对数规范对全球收缩进行分析.
- 检查离散时间储库中的激活函数属性.
- 探索连续时间库和神经常规微分方程之间的重叠.
主要成果:
- 在连续时间储库中确定了通用同步的条件.
- 制定了优化连接矩阵构建的指南.
- 证明了合约激活函数可以在离散时间储库中确保通用近似.
- 显示的连续时间储存器可以继承有限的普遍近似性质.
结论:
- 储计算,具有普遍的近似和快速训练,可以作为动态系统的强大的黑子替代品.
- 这些系统显示出在数字双胞胎中使用的潜力.
- 有效的连接矩阵设计对于实现所需的同步和近似是至关重要的.
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