标题： 多功能储层计算用于异质复杂网络 显示英文标题

标题： Versatile Reservoir Computing for Heterogeneous Complex Networks

摘要： 提出了一种新的机器学习方案，称为多功能水库计算，用于维持异质复杂网络的动力学。 我们证明，通过对元素子集的时间序列进行训练，单一的小规模水库计算机能够复制大规模复杂网络中任一元素的动力学，尽管这些元素具有不同的固有参数和连通性。 此外，通过用训练好的机器替换故障元件，我们展示了在网络的集体动力学可以在有限的时间范围内被准确地保持。 所提出的方案的能力和有效性在一个代表性网络模型集合中得到验证：一组非相同相位振荡器组成的同质复杂网络，一组非相同相位振荡器组成的异质复杂网络，以及一组非相同混沌振荡器组成的异质复杂网络。 显示英文摘要

摘要： A new machine learning scheme, termed versatile reservoir computing, is proposed for sustaining the dynamics of heterogeneous complex networks. We show that a single, small-scale reservoir computer trained on time series from a subset of elements is able to replicate the dynamics of any element in a large-scale complex network, though the elements are of different intrinsic parameters and connectivities. Furthermore, by substituting failed elements with the trained machine, we demonstrate that the collective dynamics of the network can be preserved accurately over a finite time horizon. The capability and effectiveness of the proposed scheme are validated on three representative network models: a homogeneous complex network of non-identical phase oscillators, a heterogeneous complex network of non-identical phase oscillators, and a heterogeneous complex network of non-identical chaotic oscillators.