时间分辨率对通过储库计算复制混乱动态的影响
Kohei Tsuchiyama1, André Röhm1, Takatomo Mihana1
1Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.
Chaos (Woodbury, N.Y.)
|June 22, 2023
概括
优化采样频率对于储库计算 (RC) 来自主再生混乱时间序列至关重要. 过度粗和密集的采样都会阻碍RC性能,特定的窗口被证明是最佳的.
科学领域:
- 机器学习 机器学习
- 非线性动力学是一种非线性动力学.
- 复杂的系统复杂的系统.
背景情况:
- 储库计算 (RC) 使用具有短期内存的非线性动态系统来完成高级机器学习任务.
- 在自主混乱时间序列生成,预测和分类方面,RC已经显示出前景.
- 采样是一个关键因素,特别是在需要外部数据输入的物理储库计算机中.
研究的目的:
- 分析采样对混乱时间序列自主再生的影响,使用储库计算.
- 为了确定有效的混乱时间序列再生的最佳采样频率范围.
- 根据采样密度,了解影响业绩的潜在机制.
主要方法:
- 研究了不同采样频率对水库计算性能的影响.
- 使用定量指标来评估本地和全球吸引力特征.
- 与自主再生混乱时间序列的忠实性相关的采样参数.
主要成果:
- 过于粗略的采样显著降低了混乱时间序列再生的性能.
- 过度密集的采样也被证明是不合适的,导致次于最佳的结果.
- 一个特定的,中间范围的采样频率被确定为最佳的自主再生.
结论:
- 采样频率极大地影响了储库计算能够再生混乱时间序列的能力.
- 在定义的采样频率窗口内实现最佳性能,平衡信息捕获和降噪.
- 了解这些采样效应对于设计和实施有效的物理水库计算系统至关重要.
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