Department of Physics, University of California San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA.
Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, Colorado 80305-3328, USA.
Chaos. 2021 Dec;31(12):123118. doi: 10.1063/5.0066013.
Reservoir computers (RCs) are a class of recurrent neural networks (RNNs) that can be used for forecasting the future of observed time series data. As with all RNNs, selecting the hyperparameters in the network to yield excellent forecasting presents a challenge when training on new inputs. We analyze a method based on predictive generalized synchronization (PGS) that gives direction in designing and evaluating the architecture and hyperparameters of an RC. To determine the occurrences of PGS, we rely on the auxiliary method to provide a computationally efficient pre-training test that guides hyperparameter selection. We provide a metric for evaluating the RC using the reproduction of the input system's Lyapunov exponents that demonstrates robustness in prediction.
储层计算机 (RC) 是一类递归神经网络 (RNN)，可用于预测观测时间序列数据的未来。与所有 RNN 一样，在新输入上进行训练时，选择网络中的超参数以产生出色的预测结果是一项挑战。我们分析了一种基于预测广义同步 (PGS) 的方法，该方法为设计和评估 RC 的体系结构和超参数提供了指导。为了确定 PGS 的发生，我们依赖辅助方法提供一种计算效率高的预训练测试，以指导超参数选择。我们提供了一种使用输入系统李雅普诺夫指数的再现来评估 RC 的指标，该指标在预测中表现出稳健性。
