Carroll T L
U.S. Naval Research Lab, Washington, DC 20375, USA.
Chaos. 2020 Aug;30(8):083130. doi: 10.1063/5.0014643.
Because reservoir computers are high dimensional dynamical systems, designing a good reservoir computer is difficult. In many cases, the designer must search a large nonlinear parameter space, and each step of the search requires simulating the full reservoir computer. In this work, I show that a simple statistic based on the mean path length between nodes in the reservoir computer is correlated with better reservoir computer performance. The statistic predicts the diversity of signals produced by the reservoir computer, as measured by the covariance matrix of the reservoir computer. This statistic by itself is not sufficient to predict reservoir computer performance because not only must the reservoir computer produce a diverse set of signals, it must be well matched to the training signals. Nevertheless, this path length statistic allows the designer to eliminate some network configurations from consideration without having to actually simulate the reservoir computer, reducing the complexity of the design process.
由于储层计算机是高维动力系统，设计一个良好的储层计算机具有挑战性。在许多情况下，设计者必须在一个庞大的非线性参数空间中进行搜索，并且搜索的每一步都需要对整个储层计算机进行模拟。在这项工作中，我表明基于储层计算机中节点间平均路径长度的一个简单统计量与更好的储层计算机性能相关。该统计量预测了由储层计算机产生的信号的多样性，这是通过储层计算机的协方差矩阵来衡量的。这个统计量本身并不足以预测储层计算机的性能，因为储层计算机不仅必须产生一组多样的信号，它还必须与训练信号良好匹配。然而，这个路径长度统计量允许设计者在无需实际模拟储层计算机的情况下，排除一些网络配置以供考虑，从而降低设计过程的复杂性。
