Department 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. 2023 Jun 1;33(6). doi: 10.1063/5.0143846.
Reservoir computing is a machine learning paradigm that uses a structure called a reservoir, which has nonlinearities and short-term memory. In recent years, reservoir computing has expanded to new functions such as the autonomous generation of chaotic time series, as well as time series prediction and classification. Furthermore, novel possibilities have been demonstrated, such as inferring the existence of previously unseen attractors. Sampling, in contrast, has a strong influence on such functions. Sampling is indispensable in a physical reservoir computer that uses an existing physical system as a reservoir because the use of an external digital system for the data input is usually inevitable. This study analyzes the effect of sampling on the ability of reservoir computing to autonomously regenerate chaotic time series. We found, as expected, that excessively coarse sampling degrades the system performance, but also that excessively dense sampling is unsuitable. Based on quantitative indicators that capture the local and global characteristics of attractors, we identify a suitable window of the sampling frequency and discuss its underlying mechanisms.
储层计算是一种机器学习范例，它使用一种称为储层的结构，该结构具有非线性和短期记忆。近年来，储层计算已经扩展到新的功能，如混沌时间序列的自主生成，以及时间序列预测和分类。此外，还展示了新的可能性，例如推断以前看不见的吸引子的存在。相比之下，采样对这些功能有很强的影响。由于使用现有物理系统作为储层的物理储层计算机必须进行采样，因此采样是必不可少的。本研究分析了采样对储层计算自主生成混沌时间序列能力的影响。我们如预期的那样发现，过度粗糙的采样会降低系统性能，但过于密集的采样也不合适。基于捕获吸引子局部和全局特征的定量指标，我们确定了合适的采样频率窗口，并讨论了其潜在机制。
