自主混沌时间序列预测：基于物理神经形态网络
Autonomous Chaotic Time Series Prediction using Physical Neuromorphic Networks
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中文总结 AI 辅助
本研究利用神经形态纳米线网络作为物理储备池，实现Mackey-Glass混沌时间序列的自主闭环预测，验证了无需虚拟节点增强的可行性，并展示了良好的预测精度与长期稳定性。
中文摘要 AI 辅助
物理储备池计算（PRC）结合神经形态网络，利用物理神经网络的涌现非线性动力学作为计算资源，为类脑信息处理提供了一种有前景的方法。本研究使用模拟的神经形态纳米线网络作为物理储备池，演示了Mackey-Glass（MG）混沌时间序列的全自主闭环预测。评估了两种策略：虚拟节点（VN）方法，通过时间复用储备池状态扩展特征空间；以及非VN方法，直接使用所有物理节点读数而不进行时间复用。结果针对MG时间延迟参数的两个值报告：τ=18和τ=21，后者代表更复杂的混沌状态，此前未对此类物理储备池进行评估。在T=100时间步的短预测范围内，VN方法在τ=18和τ=21时分别达到90.4%和89.7%的自主预测精度，而非VN方法分别达到81.5%和76.2%。在T=500时间步的长范围分析表明，两种方法都能再现真实MG信号的定性吸引子结构和主要频谱内容，轨迹始终保持有界。这些结果表明，神经形态纳米线网络的固有动力学足以支持有意义的自主混沌时间序列预测，无需虚拟节点增强，且随着物理网络规模扩展到硬件可实现的数百万节点，性能可能进一步提高。由于本研究使用模拟网络，外推到物理制造的大规模阵列仍需实验验证。
英文摘要
Physical reservoir computing (PRC) with neuromorphic networks offers a promising approach to brain-inspired information processing, exploiting emergent nonlinear dynamics of physical neural networks as a computational resource. This study demonstrates fully autonomous closed-loop prediction of the Mackey--Glass (MG) chaotic time series using a simulated neuromorphic nanowire network as the physical reservoir. Two strategies are evaluated: the virtual node (VN) method, which expands the feature space by temporal multiplexing of reservoir states, and a non-VN approach that uses all physical node readouts directly without temporal multiplexing. Results are reported for two values of the MG time delay parameter, $τ= 18$ and $τ= 21$, the latter representing a more complex chaotic regime not previously evaluated for this class of physical reservoir. Over a short prediction horizon of $T = 100$ timesteps, the VN approach achieves autonomous prediction accuracies of $90.4$% and $89.7$% at $τ= 18$ and $τ= 21$, respectively, while the non-VN approach achieves $81.5$% and $76.2$%. Long-horizon analysis over $T = 500$ timesteps shows that both approaches reproduce the qualitative attractor structure and dominant spectral content of the true MG signal, with trajectories remaining bounded throughout. These results suggest that the intrinsic dynamics of neuromorphic nanowire networks are sufficient to support meaningful autonomous chaotic time series prediction without virtual node augmentation, and that performance may improve further as physical network sizes scale to the millions of nodes achievable in hardware. As this study uses simulated networks, extrapolation to physically fabricated large-scale arrays remains to be validated experimentally.
发表机构
- University of Sydney（悉尼大学）
机构由 AI 辅助整理，请以论文原文为准。
