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.

翻译：储层计算是一种机器学习范式，它利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，储层计算已扩展到诸如混沌时间序列的自主生成、时间序列预测和分类等新功能。此外，还展示了诸如推断先前未见吸引子存在等新的可能性。然而，采样对这些功能有强烈影响。在利用现有物理系统作为储层的物理储层计算机中，采样是不可或缺的，因为通常不可避免地要使用外部数字系统进行数据输入。本研究分析了采样对储层计算自主再生混沌时间序列能力的影响。我们发现，正如预期的那样，过度粗糙的采样会降低系统性能，但过度密集的采样也不合适。基于捕捉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。