Jaurigue Jonnel, Robertson Joshua, Hurtado Antonio, Jaurigue Lina, Lüdge Kathy
Institut für Physik, Technische Universität Ilmenau, Ilmenau, Germany.
Institute of Photonics, SUPA Department of Physics, University of Strathclyde, Glasgow, UK.
Commun Eng. 2025 Jan 27;4(1):10. doi: 10.1038/s44172-024-00330-0.
Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.
储层计算是一种非常适合复杂时间序列预测任务的机器学习方法。延迟嵌入以及将输入数据投影到高维空间在实现准确预测方面都起着重要作用。我们建立了简单的后处理方法，这些方法在均匀或随机延迟的时间偏移下根据过去的节点状态进行训练。这些方法通过增加特征维度和/或更好的延迟嵌入来提高储层计算机的预测性能。在这里，我们介绍了多随机时间偏移方法，该方法随机召回储层节点的先前状态。多随机时间偏移的使用允许使用更小的储层，同时保持较大的特征维度，优化计算成本低，并且是我们首选的后处理方法。对于实验人员来说，我们所有的后处理方法都可以转化为从物理储层采样的读出数据，我们使用来自实验实现的激光储层系统的读出数据进行了演示。
