论文标题
成功储层计算的参数选择的限制
Constraints on parameter choices for successful reservoir computing
论文作者
论文摘要
回声状态网络是由时间序列驱动的离散动力系统的简单模型。通过选择网络参数，以使网络的动力学是合同的，其特征在于负Lyapunov指数，网络可以与驱动信号同步。利用此同步，可以对回声网络进行训练以自主再现输入动力学，从而实现时间序列的预测。但是，尽管同步是预测的必要条件，但这还不够。在这里，我们研究成功的时间序列预测还需要哪些其他条件。我们确定了预测性能的两个关键参数，并进行参数扫描以查找预测成功的区域。这些区域的差异很大，具体取决于在培训期间向网络提供有关输入的完整或部分相位空间信息。我们解释了这些地区是如何出现的。
Echo-state networks are simple models of discrete dynamical systems driven by a time series. By selecting network parameters such that the dynamics of the network is contractive, characterized by a negative maximal Lyapunov exponent, the network may synchronize with the driving signal. Exploiting this synchronization, the echo-state network may be trained to autonomously reproduce the input dynamics, enabling time-series prediction. However, while synchronization is a necessary condition for prediction, it is not sufficient. Here, we study what other conditions are necessary for successful time-series prediction. We identify two key parameters for prediction performance, and conduct a parameter sweep to find regions where prediction is successful. These regions differ significantly depending on whether full or partial phase space information about the input is provided to the network during training. We explain how these regions emerge.
