发表机构
Loughborough University（拉夫堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文通过纳米多孔氧化铌储备池，研究噪声相关时间、储备池记忆与预测范围的关系，提出时间尺度匹配指导物理储备池架构，实现相关噪声的滤波与预测。
AI 中文摘要
物理储备池计算利用物理系统的非线性动力学处理时间相关数据，其能效高于传统机器学习方法。然而，物理储备池具有固定的固有响应时间尺度，而真实世界信号在多个时间尺度上结合了确定性与随机性成分。本文使用纳米多孔氧化铌储备池、合成噪声信号和加密货币价格波动性表明，噪声相关时间、储备池记忆与预测范围之间的关系决定了相关噪声是被滤波还是被预测。变化快于相关储备池记忆和预测范围的噪声会被储备池平均化，而较慢变化噪声的时间结构足以用于算法预测。我们引入了储备池记忆范围和预测机制指数来区分这些运行机制。这些贡献表明，时间尺度匹配可以指导输入时间序列的编码以及物理储备池架构的开发，以在跨不同时间尺度上滤波、分析和预测随机信号成分。
英文摘要
Physical reservoir computing exploits the nonlinear dynamics of physical systems to process time-dependent data with greater energy efficiency than conventional machine learning approaches. However, physical reservoirs have fixed intrinsic response timescales, whereas real-world signals combine deterministic and stochastic components across multiple timescales. Here we show, using a nanoporous niobium oxide reservoir, synthetic noisy signals and cryptocurrency-price volatility, that the relationship among noise correlation time, reservoir memory and forecast horizon determines whether correlated noise is filtered or predicted. Noise varying faster than the relevant reservoir memory and forecast horizon is averaged by the reservoir, whereas the temporal structure of slower-varying noise is sufficient for algorithmic forecasting. We introduce the reservoir memory horizon and forecasting regime index to distinguish these operating regimes. These contributions demonstrate that timescale matching can guide the encoding of input time series and development of physical reservoir architectures that filter, analyse and predict stochastic signal components across distinct temporal scales.
