arXiv：cs.LG· Joshua Donald, Alex Gabbitas, Arthur G. T. Coveney, Sergey Savel'ev, Pavel Borisov·· 4 小时前AI 评分33
物理储备池计算中信号、噪声与硬件时间尺度的匹配：过滤与预测相关噪声信号
Matching of signal, noise and hardware timescales for filtering and forecasting of correlated noise signals
AI 导读
研究利用纳米多孔氧化铌储备池，揭示噪声相关时间、储备池记忆与预测视野三者关系决定相关噪声是被过滤还是被预测：变化快于储备池记忆和预测视野的噪声被平均，变化较慢的噪声则可用于算法预测。作者提出储备池记忆视野和预测状态指数来区分这两种工作模式，并在合成噪声信号和加密货币价格波动数据上验证。该结果可为物理储备池架构的输入编码设计提供时间尺度匹配指导。
正文
Abstract: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.
| Subjects: | Machine Learning (cs.LG); Disordered Systems and Neural Networks (cond-mat.dis-nn); Applied Physics (physics.app-ph); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2610.10037 [cs.LG] |
| (or arXiv:2610.10037v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10037 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Pavel Borisov [view email]
[v1]
Wed, 7 Oct 2026 13:18:20 UTC (4,872 KB)
来源：arXiv：cs.LG · arxiv.org